The Data Product Management In Action podcast, brought to you by  executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. In Season 01, Episode 28, we are excited to introduce to you a new host, Alexa Westgate! Join us as we learn all about her data journey. She'll discuss how she got into DPM, some of her greatest moments and challenges. You'll be excited for her future episodes! About our host Alexa Westlake: Alexa is a Data Analytics Leader in the Identity and Access Management space with a proven track record scaling high-growth SaaS companies. As a Staff Data Analyst at Okta, she brings a wealth of expertise in enterprise data, business intelligence, and strategic decision-making from the various industries she's worked in including telecommunications, strategy execution, and cloud computing. With a passion for harnessing the power of data for actionable insights, Alexa plays a crucial role in driving Okta's security, growth, and scale, helping the organization leverage data to execute on their market opportunity. Connect with Alexa on LinkedIn. All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else.  Join the conversation on LinkedIn.  Apply to be a guest or nominate someone that you know.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights! 

The Data Product Management In Action podcast, brought to you by executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. In the 25th celebration minisode of Data Product Management in Action, hosts Frannie Helforoush and Nadiem von Heydebrand reflect on the progress of data product management in 2024. They highlight the growing clarity and recognition of the field, the rise of AI product management, and the importance of thoughtful integration without succumbing to overhype. The episode revisits key 2024 discussions on building data platforms, decision support products, and data mesh implementation. Looking forward to 2025, they foresee increased interest and adoption, emphasizing the field's potential for driving organizational value. Frannie and Nadiem express excitement for future episodes and community contributions. About our Host Nadiem von Heydebrand: Nadiem is CEO and Co-Founder at Mindfuel. In 2019, he combined his passion for data science with product management and is a thought leader for data product management today, aiming to prove true value contribution from data. Working as an expert in the data industry for over a decade now, he has seen hundreds of data science initiatives, built scaled data teams and enabled global organizations like Volkswagen, Munich Re, Allianz, Red Bull, Vorwerk to become data-driven. With Mindfuel “Delight”, a Data Product Management SaaS solution combined with professional services, he brought in experience from hands-on challenges like scaling out data platforms and architecture, implementing data mesh concepts or transforming AI performance into business performance to delight consumers all over the globe. Connect with Nadiem on LinkedIn

About our Host Frannie Helforoush: From coding to crafting customer-centric products, my journey began as a software engineer and evolved into a strategic product manager. With an innate curiosity for problem-solving, I fuse my expertise in data and product management to create impactful solutions as a data product manager now. With a background in both software engineering and product management, I seamlessly bridge the gap between the data and product worlds. I thrive on making data accessible and actionable for driving product innovation and  ensuring that product thinking is applied to every aspect of data management. Connect with Frannie on LinkedIn All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else.  Join the conversation on LinkedIn.  Apply to be a guest or nominate someone that you know.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights!  

Supported by Our Partners • Vanta — Automate compliance and simplify security with Vanta. • WorkOS — The modern identity platform for B2B SaaS. — In today’s episode of The Pragmatic Engineer, I’m joined by Michael Novati, Co-founder and CTO of Formation. Before launching Formation, Michael spent eight years at Meta, where he was recognized as the top code committer company-wide for several years. The “Coding Machine” archetype was modeled after Michael at the company. In our conversation, we talk about what it was like working at Meta and dive into its engineering culture. Michael shares his journey of quickly climbing the ranks from intern to principal-level and gives level-headed advice on leveling up your career. Plus, we discuss his work at Formation, where he helps engineers grow and land roles at top tech companies. In this episode, we cover: • An overview of software architect archetypes at Meta, including “the coding machine” • Meta’s org structure, levels of engineers, and career trajectories • The importance of maintaining a ‘brag list’ to showcase your achievements and impact • Meta’s engineering culture and focus on building internal tools • How beating Mark Zuckerberg in a game of Risk led to him accepting Michael’s friend request • An inside look at Meta’s hiring process • Tips for software engineers on the job market on how to do better in technical interviews • And more! — Timestamps (00:00) Intro (01:45) An explanation of archetypes at Meta, including “the coding machine” (09:14) The organizational structure and levels of software engineers at Meta (10:05) Michael’s first project re-writing the org chart as an intern at Meta (12:42) A brief overview of Michael’s work at Meta  (15:29) Meta’s engineering first culture and how Michael pushed for even more for ICs (20:03) How tenure at Meta correlated with impact  (23:47) How Michael rose through the ranks at Meta so quickly (29:30) The engineering culture at Meta, including how they value internal tools (34:00) Companies that began at Meta or founded by former employees (36:11) Facebook’s internal tool for scheduling meetings  (37:45) The product problems that came with scaling Facebook (39:25) How Michael became Facebook friends with Mark Zuckerberg  (42:05) The “Zuck review” process (44:30) How the French attacks crashed Michael’s photo inlay prototype (51:15) How the photo inlay bug was fixed  (52:58) Meta’s hiring process  (1:03:40) Insights from Michael’s work at Formation (1:09:08) Michael’s advice for experienced engineers currently searching for a job (1:11:15) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Inside Meta’s engineering culture: https://newsletter.pragmaticengineer.com/p/facebook • Stacked diffs (and why you should know about them) https://newsletter.pragmaticengineer.com/p/stacked-diffs  • Engineering career paths at Big Tech and scaleups: https://newsletter.pragmaticengineer.com/p/engineering-career-paths  • Inside the story of how Meta built the Threads app: https://newsletter.pragmaticengineer.com/p/building-the-threads-app — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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This talk is about using synthetic monitoring to reduce MTTD&MTTR significantly and achieve high devops maturity. Daniel is a big believer in synthetic monitoring as a concept to build reliable production services. If engineers are supposed to run what they build, they need monitoring tools that work for them. He has built his own custom solutions in the past using Jenkins or GH Actions and later used SaaS tools for this. He would like to share his experience getting frontend engineers to build monitoring and get everyone on an engineering team to care about production system reliability. Daniel Paulus has taken a unique journey from military officer to tech leader, and he’s now the VP of Engineering at Checkly. Along the way, he’s worn many hats— from engineering lead to director —learning how to build strong teams and solve tough challenges. Outside of work, Daniel lives near Berlin with his family and four kids, while also finding time to maintain an open-source project. Whether it’s scaling teams or debugging code, he’s passionate about technology and enjoys sharing his knowledge with others.

In today’s data-driven landscape, organizations rely on enterprise applications to manage critical business processes. However, extracting and integrating this data into data warehouses and data lakes can be complex. This session explores a new zero-ETL capability that simplifies ingesting data to Amazon SageMaker Lakehouse and Amazon Redshift via AWS Glue from enterprise applications such as Salesforce, ServiceNow, and Zendesk. See how zero-ETL automates the extract and load process, expanding your analytics and machine solutions with valuable SaaS data.

Learn more: AWS re:Invent: https://go.aws/reinvent. More AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

About AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2024

Hear from two AWS customers as they discuss their journey migrating to Amazon Bedrock and finding success in developing generative AI applications on AWS. Leaders from Forcura, a healthcare workflow management company headquartered in Jacksonville, Florida, share how they facilitate continuity of care and improve business performance for providers using automated workflows, collaboration, and analytics SaaS solutions. Additionally, leaders from Cencosud S.A., the largest retail company in Chile and second-largest in LATAM, discuss how they created an assistant to support their grocery customers' digital shopping journeys.

Learn more: AWS re:Invent: https://go.aws/reinvent. More AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

About AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2024

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here. What makes a database modern, and why does it matter? In a world where we face countless choices, how do you build systems that not only scale but also make life easier for your teams? And with AI reshaping industries and workflows, how do businesses bridge the gap between legacy systems and cutting-edge applications? Sahir Azam is the Chief Product Officer at MongoDB. He has been with MongoDB since 2016, where he launched the industry’s first developer data platform, MongoDB Atlas, and scaled the company’s thriving cloud business from the ground up. He also serves on the boards of Temporal and Observe, Inc, a cloud data observability startup. Sahir joined MongoDB from Sumo Logic, where he managed platform, pricing, packaging, and technology partnerships. Before Sumo Logic, he launched VMware's first organically developed SaaS management product and grew their management tools business to $1B+ in revenue. Earlier in his career, Sahir also held technical and sales-focused roles at DynamicOps, BMC Software, and BladeLogic. In the episode, Richie and Sahir Azam explore the evolution of databases beyond NoSQL, enhancing developer productivity, integrating AI capabilities, modernizing legacy systems, and much more. Links Mentioned in the Show: MongoDBConnect with SahirCourse: Introduction to MongoDB in PythonRelated Episode: Not Only Vector Databases: Putting Databases at the Heart of AI, with Andi Gutmans, VP and GM of Databases at GoogleRewatch sessions from RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Brought to you by: • WorkOS — The modern identity platform for B2B SaaS. • Sevalla — Deploy anything from preview environments to Docker images. • Chronosphere — The observability platform built for control. — Welcome to The Pragmatic Engineer! Today, I’m thrilled to be joined by Grady Booch, a true legend in software development. Grady is the Chief Scientist for Software Engineering at IBM, where he leads groundbreaking research in embodied cognition. He’s the mind behind several object-oriented design concepts, a co-author of the Unified Modeling Language, and a founding member of the Agile Alliance and the Hillside Group. Grady has authored six books, hundreds of articles, and holds prestigious titles as an IBM, ACM, and IEEE Fellow, as well as a recipient of the Lovelace Medal (an award for those with outstanding contributions to the advancement of computing). In this episode, we discuss: • What it means to be an IBM Fellow • The evolution of the field of software development • How UML was created, what its goals were, and why Grady disagrees with the direction of later versions of UML • Pivotal moments in software development history • How the software architect role changed over the last 50 years • Why Grady declined to be the Chief Architect of Microsoft – saying no to Bill Gates! • Grady’s take on large language models (LLMs) • Advice to less experienced software engineers • … and much more! — Timestamps (00:00) Intro (01:56) What it means to be a Fellow at IBM (03:27) Grady’s work with legacy systems (09:25) Some examples of domains Grady has contributed to (11:27) The evolution of the field of software development (16:23) An overview of the Booch method (20:00) Software development prior to the Booch method (22:40) Forming Rational Machines with Paul and Mike (25:35) Grady’s work with Bjarne Stroustrup (26:41) ROSE and working with the commercial sector (30:19) How Grady built UML with Ibar Jacobson and James Rumbaugh (36:08) An explanation of UML and why it was a mistake to turn it into a programming language (40:25) The IBM acquisition and why Grady declined Bill Gates’s job offer  (43:38) Why UML is no longer used in industry  (52:04) Grady’s thoughts on formal methods (53:33) How the software architect role changed over time (1:01:46) Disruptive changes and major leaps in software development (1:07:26) Grady’s early work in AI (1:12:47) Grady’s work with Johnson Space Center (1:16:41) Grady’s thoughts on LLMs  (1:19:47) Why Grady thinks we are a long way off from sentient AI  (1:25:18) Grady’s advice to less experienced software engineers (1:27:20) What’s next for Grady (1:29:39) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • The Past and Future of Modern Backend Practices https://newsletter.pragmaticengineer.com/p/the-past-and-future-of-backend-practices  • What Changed in 50 Years of Computing https://newsletter.pragmaticengineer.com/p/what-changed-in-50-years-of-computing  • AI Tooling for Software Engineers: Reality Check https://newsletter.pragmaticengineer.com/p/ai-tooling-2024 — Where to find Grady Booch: • X: https://x.com/grady_booch • LinkedIn: https://www.linkedin.com/in/gradybooch • Website: https://computingthehumanexperience.com Where to find Gergely: • Newsletter: https://www.pragmaticengineer.com/ • YouTube: https://www.youtube.com/c/mrgergelyorosz • LinkedIn: https://www.linkedin.com/in/gergelyorosz/ • X: https://x.com/GergelyOrosz — References and Transcripts: See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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Microsoft Fabric is the unified, open, governed, and AI-powered analytics platform fueling your next wave of business innovation. We're excited to share a full slate of innovations with customers and partners, including the introduction of the first-in-category full SaaS databases in Fabric. Hear from the Fabric leadership team on the progress we’ve made, the new ground we’re breaking, and why we believe Fabric can accelerate your organization's transformation in the era of AI.

𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀: * Amir Netz * Arun Ulagaratchagan

𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: This is one of many sessions from the Microsoft Ignite 2024 event. View even more sessions on-demand and learn about Microsoft Ignite at https://ignite.microsoft.com

BRK204 | English (US) | Data

MSIgnite

Explore how Docusign is transforming business with its new SaaS Intelligent Agreement Management platform. Docusign will show how they architected their platform with Azure AI, data, and app services to automate agreement workflows and surface insights from business data. Learn how Docusign is driving innovation and achieving global scale with Azure. This is a must-attend session for enterprise architects looking to learn from real-world examples how to build AI-powered SaaS apps in the cloud.

𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀: * Deborah Chen * Kunal Mukerjee * Olivia Shone

𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: This is one of many sessions from the Microsoft Ignite 2024 event. View even more sessions on-demand and learn about Microsoft Ignite at https://ignite.microsoft.com

BRK124 | English (US) | AI

MSIgnite

R&D for materials-based products can be expensive, because improving a product’s materials takes a lot of experimentation that historically has been slow to execute. In traditional labs, you might change one variable, re-run your experiment, and see if the data shows improvements in your desired attributes (e.g. strength, shininess, texture/feel, power retention, temperature, stability, etc.). However, today, there is a way to leverage machine learning and AI to reduce the number of experiments a material scientist needs to run to gain the improvements they seek. Materials scientists spend a lot of time in the lab—away from a computer screen—so how do you design a desirable informatics SAAS that actually works, and fits into the workflow of these end users?    

As the Chief Product Officer at MaterialsZone, Ori Yudilevich came on Experiencing Data with me to talk about this challenge and how his PM, UX, and data science teams work together to produce a SAAS product that makes the benefits of materials informatics so valuable that materials scientists depend on their solution to be time and cost-efficient with their R&D efforts.   

We covered:

(0:45) Explaining what Ori does at MaterialZone and who their product serves (2:28) How Ori and his team help make material science testing more efficient through their SAAS product (9:37) How they design a UX that can work across various scientific domains (14:08) How “doing product” at MaterialsZone matured over the past five years (17:01) Explaining the "Wizard of Oz" product development technique (21:09) The importance of integrating UX designers into the "Wizard of Oz" (23:52) The challenges MaterialZone faces when trying to get users to adopt to their product (32:42) Advice Ori would've given himself five years ago (33:53) Where you can find more from MaterialsZone and Ori

Quotes from Today’s Episode

“The fascinating thing about materials science is that you have this variety of domains, but all of these things follow the same process. One of the problems [consumer goods companies] face is that they have to do lengthy testing of their products. This is something you can use machine learning to shorten. [Product research] is an iterative process that typically takes a long time. Using your data effectively and using machine learning to predict what can happen, what’s better to try out, and what will reduce costs can accelerate time to market.” - Ori Yudilevich (3:47) “The difference [in time spent testing a product] can be up to 70% [i.e. you can run 70% fewer experiments using ML.]  That [also] means 70% less resources you’re using. Under the ‘old system’ of trial and error, you were just trying out a lot of things. The human mind cannot process a large number of parameters at once, so [a materials scientist] would just start playing only with [one parameter at a time]. You’ll have many experiments where you just try to optimize [for] one parameter, but then you might have 20, 30, or 100 more [to test]. Using machine learning, you can change a lot of parameters at once. The model can learn what has the most effect, what has a positive effect, and what has a negative effect. The differences can be really huge.” - Ori Yudilevich (5:50) “Once you go deeper into a use case, you see that there are a lot of differences. The types of raw materials, the data structure, the quantity of data, etc. For example, with batteries, you have lots of data because you can test hundreds all at once. Whereas with something like ceramics, you don’t try so many [experiments]. You just can’t. It’s much slower. You can’t do so many [experiments] in parallel. You have much less data. Your models are different, and your data structure is different. But there’s also quite a lot of commonality because you’re storing the data. In the end, you have each domain, some raw materials, formulations, tests that you’re doing, and different statistical plots that are very common.” - Ori Yudilvech (11:24) “We’ll typically do what we call the ‘Wizard of Oz’ technique. You simulate as if you have a feature, but you’re actually working for your client behind the scenes. You tell them [the simulated feature] is what you’re doing, but then measure [the client’s response] to understand if there’s any point in further developing that feature. Once you validate it, have enough data, and know where the feature is going, then you’ll start designing it and releasing it in incremental stages. We’ve made a lot of progress in how we discover opportunities and how we build something iteratively to make sure that we’re always going in the right direction” - Ori Yudilevich (15:56) “The main problem we’re encountering is changing the mindset of users. Our users are not people who sit in front of a computer. These are researchers who work in [a materials science] lab. The challenge [we have] is getting people to use the platform more. To see it’s worth [their time] to look at some insights, and run the machine learning models. We’re always looking for ways to make that transition faster… and I think the key is making [the user experience] just fun, easy, and intuitive.” - Ori Yudilevich (24:17) “Even if you make [the user experience] extremely smooth, if [users] don’t see what they get out of it, they’re still not going to [adopt your product] just for the sake of doing it. What we find is if this [product] can actually make them work faster or develop better products– that gets them interested. If you’re adopting these advanced tools, it makes you a better researcher and worker. People who [adopt those tools] grow faster. They become leaders in their team, and they slowly drag the others in.” - Ori Yudilevich (26:55) “Some of [MaterialsZone’s] most valuable employees are the people who have been users. Our product manager is a materials scientist. I’m not a material scientist, and it’s hard to imagine being that person in the lab. What I think is correct turns out to be completely wrong because I just don’t know what it’s like. Having [material scientists] who’ve made the transition to software and data science? You can’t replace that.” - Ori Yudilevich (31:32)

Links Referenced Website: https://www.materials.zone

LinkedIn: https://www.linkedin.com/in/oriyudilevich/

Email: [email protected]

Introducing SQL database in Fabric: Discover the future of data management and unlock new scenarios that drive your business forward. Join us as we showcase the first fully SaaS database experience in Microsoft Fabric. Experience the simplicity of an integrated development environment that empowers you to quickly harness the power of an AI-driven analytics platform. Learn how to access both transactional and analytical data in one place without compromising application performance.

𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀: * Panos/Panagiotis Antonopoulos * Anna Hoffman * Asad Khan

𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: This is one of many sessions from the Microsoft Ignite 2024 event. View even more sessions on-demand and learn about Microsoft Ignite at https://ignite.microsoft.com

BRK196 | English (US) | Data

MSIgnite

Brought to you by: • Launch Darkly — a platform for high-velocity engineering teams to release, monitor, and optimize great software.  • Sevalla — Deploy anything from preview environments to Docker images. • WorkOS — The modern identity platform for B2B SaaS. — On today’s episode of The Pragmatic Engineer, I’m joined by fellow Uber alum, Sabin Roman, now the first Engineering Manager at Linear. Linear, known for its powerful project and issue-tracking system, streamlines workflows throughout the product development process. In our conversation today, Sabin and I compare building projects at Linear versus our experiences at Uber. He shares insights into Linear’s unique approaches, including: • How Linear handles internal communications • The “goalie” program to address customer concerns and Linear’s zero bug policy • How Linear keeps teams connected despite working entirely remotely • An in-depth, step-by-step walkthrough of a project at Linear • Linear’s focus on quality and creativity over fash shipping  • Titles at Linear, Sabin’s learnings from Uber, and much more! Timestamps (00:00) Intro (01:41) Sabin’s background (02:45) Why Linear rarely uses e-mail internally (07:32) An overview of Linear's company profile (08:03) Linear’s tech stack (08:20) How Linear operated without product people (09:40) How Linear stays close to customers (11:27) The shortcomings of Support Engineers at Uber and why Linear’s “goalies” work better (16:35) Focusing on bugs vs. new features (18:55) Linear’s hiring process (21:57) An overview of a typical call with a hiring manager at Linear (24:13) The pros and cons of Linear’s remote work culture (29:30) The challenge of managing teams remotely (31:44) A step-by-step walkthrough of how Sabin built a project at Linear  (45:47) Why Linear’s unique working process works  (49:57) The Helix project at Uber and differences in operations working at a large company (57:47) How senior engineers operate at Linear vs. at a large company (1:01:30) Why Linear has no levels for engineers  (1:07:13) Less experienced engineers at Linear (1:08:56) Sabin’s big learnings from Uber (1:09:47) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • The story of Linear, as told by its CTO • An update on Linear, after their $35M fundraise • Software engineers leading projects • Netflix’s historic introduction of levels for software engineers — Where to find Sabin Roman: • X: https://x.com/sabin_roman • LinkedIn: https://www.linkedin.com/in/sabinroman/ Where to find Gergely: • Newsletter: https://www.pragmaticengineer.com/ • YouTube: https://www.youtube.com/c/mrgergelyorosz • LinkedIn: https://www.linkedin.com/in/gergelyorosz/ • X: https://x.com/GergelyOrosz — References and Transcripts: See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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Brought to you by: • WorkOS — The modern identity platform for B2B SaaS. • Sonar —  Trust your developers – verify your AI-generated code. — In today’s episode of The Pragmatic Engineer, I’m joined by Irina Stanescu, a seasoned engineer with over 14 years in software engineering and engineering leadership roles at tech companies like Google and Uber. Now an engineering leadership coach, Irina helps tech professionals build impactful careers, teaches a course on influence, and shares insights through her newsletter, The Caring Techie. In our conversation today, Irina shares her journey of rising through the ranks at Google and Uber. We dive into the following topics:  • An inside look at Google’s unique working processes • How to build credibility as a new engineer • Tactical tips for getting promoted  • The importance of having a career plan and guidance in designing one • Having influence vs. influencing—and how to become more influential  • Essential leadership skills to develop • And so much more — In this episode, we cover: (01:34) Irina’s time at Google (03:10) An overview of ‘design docs’ at Google (08:27) The readiness review at Google (10:40) Why Irina uses spreadsheets (11:44) Irina’s favorite tools and how she uses them (13:46) How Google certifies readability (15:40) Google’s meme generator  (17:36) Advice for engineers thinking about working for an organization like Google (20:14) How promotions work at Google (23:15) How Irina worked towards getting promoted  (27:50) How Irina got her first mentor  (30:44) Organizational shifts at Uber while Irina and Gergely were there (35:50) Why you should prioritize growth over promotion (36:50) What a career plan is and how to build one (40:40) Irina’s current role coaching engineers  (42:23) A simple explanation of influence and influencing  (51:54) Why saying no is necessary at times (54:30) The importance of building leadership skills — The Pragmatic Engineer deepdives relevant for this episode: • Preparing for promotions ahead of time: https://newsletter.pragmaticengineer.com/p/preparing-for-promotions  • Engineering career paths at Big Tech and scaleups: https://newsletter.pragmaticengineer.com/p/engineering-career-paths  • Getting an Engineering Executive Job: https://newsletter.pragmaticengineer.com/p/getting-an-engineering-executive  • The Seniority Rollercoaster: https://newsletter.pragmaticengineer.com/p/the-seniority-rollercoaster — Where to find Irina Stanescu: • X: https://x.com/thecaringtechie • LinkedIn: https://www.linkedin.com/in/irinastanescu/ • Website: https://www.thecaringtechie.com/ • Maven course: Impact through Influence in Engineering Teams: https://maven.com/irina-stanescu/influence-swe Where to find Gergely: • Newsletter: https://www.pragmaticengineer.com/ • YouTube: https://www.youtube.com/c/mrgergelyorosz • LinkedIn: https://www.linkedin.com/in/gergelyorosz/ • X: https://x.com/GergelyOrosz — References and Transcripts: See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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En el primer episodio de Chicas en IA, aprenderás sobre los conceptos básicos de la programación en la nube. Comprenderás qué son las nubes públicas, privadas e híbridas, los beneficios y los tipos de servicios, como IaaS, PaaS, SaaS, Serverless, y cómo usar Azure para desarrollar tus aplicaciones.

Brought to you by: • The Enterprise Ready Conference on October 30th — For B2B leaders building enterprise SaaS. • DX — DX is an engineering intelligence platform designed by leading researchers.  • ByteByteGo — Ace your next system design interview. — You may not be familiar with Bending Spoons, but I guarantee you’ve encountered some of their well-known products, like Evernote and Meetup. In today’s episode of The Pragmatic Engineer, we sit down with three key figures from the Italy-based startup: cofounder and CEO Luca Ferrari, CTO Francesco Mancone, and Evernote product lead Federico Simionato. Bending Spoons has been profitable from day one, and there's plenty we can learn from their unique culture, organizational structure, engineering processes, and hiring practices. In today’s conversation, we cover the following topics: • The controversial acquisitions approach of Bending Spoons • How Bending Spoons spent more than $1 billion in buying tech companies • How the Evernote acquisition happened • How Bending Spoons operates and how it organizes product and platform teams • Why engineering processes are different across different products • How ‘radical simplicity’ is baked into everything from engineering processes to pay structure. • And much more! — The Pragmatic Engineer deepdives relevant for this episode: • Good attrition, bad attrition for software engineers: https://newsletter.pragmaticengineer.com/p/attrition  • Healthy oncall practices: https://newsletter.pragmaticengineer.com/p/healthy-oncall-practices • Shipping to production: https://newsletter.pragmaticengineer.com/p/shipping-to-production • QA across the tech industry: https://newsletter.pragmaticengineer.com/p/qa-across-tech — In this episode, we cover: (2:09) Welcome, Luca, Francesco, and Federico from Bending Spoons (03:15) An overview of the well-known apps and products owned by Bending Spoons (06:38) The elephant in the room: how Bending Spoons really acquires companies (09:46) Layoffs: Bending Spoons’ philosophy on this (14:10) Controversial principles (17:16) Revenue, team size, and products (19:35) How Bending Spoons runs AI products and allocates GPUs (23:05) History of the company (27:04) The Evernote acquisition (29:50) Modernizing Evernote’s infrastructure (32:44) “Radical simplicity” and why they try for zero on calls (36:13) More on changes made to the Evernote systems (41:13) How Bending Spoons prioritizes and ships fast  (49:40) What’s new and what’s coming for Bending Spoons (51:08) Organizational structure at the company (54:07) Engineering practices (57:03) Testing approaches (58:53) Platform teams (1:01:52) Bending Spoons tech stack and popular frameworks (1:05:55) Why Bending Spoons hires new grads and less experienced engineers (1:08:09) The structure of careers and titles at Bending Spoons (1:09:50) Traits they look for when hiring  (1:12:50) Why there aren’t many companies doing what Bending Spoons does  — Where to find Luca Ferrari: • X: https://x.com/luke10ferrari • LinkedIn: https://www.linkedin.com/in/luca-ferrari-12418318 Where to find  Francesco Mancone: • LinkedIn: https://www.linkedin.com/in/francesco-mancone Where to find Federico Simionato: • X: https://x.com/fedesimio • LinkedIn: https://www.linkedin.com/in/federicosimionato Where to find Gergely: • Newsletter: https://www.pragmaticengineer.com/ • YouTube: https://www.youtube.com/c/mrgergelyorosz • LinkedIn: https://www.linkedin.com/in/gergelyorosz/ • X: https://x.com/GergelyOrosz — References and Transcripts: See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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Sometimes DIY UI/UX design only gets you so far—and you know it’s time for outside help. One thing prospects from SAAS analytics and data-related product companies often ask me is how things are like in the other guy/gal’s backyard. They want to compare their situation to others like them. So, today, I want to share some of the common “themes” I see that usually are the root causes of what leads to a phone call with me. 

By the time I am on the phone with most prospects who already have a product in market, they’re usually either having significant problems with 1 or more of the following: sales friction (product value is opaque); low adoption/renewal worries (user apathy), customer complaints about UI/UX being hard to use; velocity (team is doing tons of work, but leader isn’t seeing progress)—and the like. 

I’m hoping today’s episode will explain some of the root causes that may lead to these issues — so you can avoid them in your data product building work!  

Highlights/ Skip to:

(10:47) Design != "front-end development" or analyst work (12:34)  Liking doing UI/UX/viz design work vs. knowing  (15:04)  When a leader sees lots of work being done, but the UX/design isn’t progressing (17:31) Your product’s UX needs to convey some magic IP/special sauce…but it isn’t (20:25) Understanding the tradeoffs of using libraries, templates, and other solution’s design as a foundation for your own  (25:28) The sunk cost bias associated with POCs and “we’ll iterate on it” (28:31) Relying on UI/UX "customization" to please all customers (31:26) The hidden costs of abstraction of system objects, UI components, etc.  to make life easier for engineering and technical teams (32:32) Believing you’ll know the design is good “when you see it” (and what you don’t know you don’t know) (36:43) Believing that because the data science/AI/ML modeling under your solution was, accurate, difficult, and/or expensive makes it automatically worth paying for 

Quotes from Today’s Episode The challenge is often not knowing what you don’t know about a project. We often end up focusing on building the tech [and rushing it out] so we can get some feedback on it… but product is not about getting it out there so we can get feedback. The goal of doing product well is to produce value, benefits, or outcomes. Learning is important, but that’s not what the objective is. The objective is benefits creation. (5:47) When we start doing design on a project that’s not design actionable, we build debt and sometimes can hurt the process of design. If you start designing your product with an entire green space, no direction, and no constraints, the chance of you shipping a good v1 is small. Your product strategy needs to be design-actionable for the team to properly execute against it. (19:19) While you don’t need to always start at zero with your UI/UX design, what are the parts of your product or application that do make sense to borrow , “steal” and cheat from? And when does it not?  It takes skill to know when you should be breaking the rules or conventions. Shortcuts often don’t produce outsized results—unless you know what a good shortcut looks like.  (22:28) A proof of concept is not a minimum valuable product. There’s a difference between proving the tech can work and making it into a product that’s so valuable, someone would exchange money for it because it’s so useful to them. Whatever that value is, these are two different things. (26:40) Trying to do a little bit for everybody [through excessive customization] can often result in nobody understanding the value or utility of your solution. Customization can hide the fact the team has decided not to make difficult choices. If you’re coming into a crowded space… it’s like’y not going to be a compelling reason to [convince customers to switch to your solution]. Customization can be a tax, not a benefit. (29:26) Watch for the sunk cost bias [in product development]. [Buyers] don’t care how the sausage was made. Many don’t understand how the AI stuff works, they probably don’t need to understand how it works. They want the benefits downstream from technology wrapped up in something so invaluable they can’t live without it.  Watch out for technically right, effectively wrong. (39:27)

Brought to you by: • Paragon: ​​Build native, customer-facing SaaS integrations 7x faster. • WorkOS: For B2B leaders building enterprise SaaS — On today’s episode of The Pragmatic Engineer, I’m joined by Quinn Slack, CEO and co-founder of Sourcegraph, a leading code search and intelligence platform. Quinn holds a degree in Computer Science from Stanford and is deeply passionate about coding: to the point that he still codes every day! He also serves on the board of Hack Club, a national nonprofit dedicated to bringing coding clubs to high schools nationwide. In this insightful conversation, we discuss:             • How Sourcegraph's operations have evolved since 2021 • Why more software engineers should focus on delivering business value • Why Quinn continues to code every day, even as a CEO • Practical AI and LLM use cases and a phased approach to their adoption • The story behind Job Fairs at Sourcegraph and why it’s no longer in use • Quinn’s leadership style and his focus on customers and product excellence • The shift from location-independent pay to zone-based pay at Sourcegraph • And much more! — Where to find Quinn Slack: • X: https://x.com/sqs • LinkedIn: https://www.linkedin.com/in/quinnslack/ • Website: https://slack.org/ Where to find Gergely: • Newsletter: https://www.pragmaticengineer.com/ • YouTube: https://www.youtube.com/c/mrgergelyorosz • LinkedIn: https://www.linkedin.com/in/gergelyorosz/ • X: https://x.com/GergelyOrosz — In this episode, we cover: (01:35) How Sourcegraph started and how it has evolved over the past 11 years (04:14) How scale-ups have changed  (08:27) Learnings from 2021 and how Sourcegraph’s operations have streamlined (15:22) Why Quinn is for gradual increases in automation and other thoughts on AI (18:10) The importance of changelogs (19:14) Keeping AI accountable and possible future use cases  (22:29) Current limitations of AI (25:08) Why early adopters of AI coding tools have an advantage  (27:38) Why AI is not yet capable of understanding existing codebases  (31:53) Changes at Sourcegraph since the deep dive on The Pragmatic Engineer blog (40:14) The importance of transparency and understanding the different forms of compensation (40:22) Why Sourcegraph shifted to zone-based pay (47:15) The journey from engineer to CEO (53:28) A comparison of a typical week 11 years ago vs. now (59:20) Rapid fire round The Pragmatic Engineer deepdives relevant for this episode: • Inside Sourcegraph’s engineering culture: Part 1 https://newsletter.pragmaticengineer.com/p/inside-sourcegraphs-engineering-culture• Inside Sourcegraph’s engineering culture: Part 2 https://newsletter.pragmaticengineer.com/p/inside-sourcegraphs-engineering-culture-part-2 — References and Transcript: See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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Welcome to the Data Engineering Central Podcast —— a no-holds-barred discussion on the Data Landscape. Welcome to Episode 02 In today’s episode, we will talk about the following topics from the Data Engineering perspective … * Using OpenAI’s o1 Model to do Data Engineering work * Lord Save us from more ETL tools * Rust for the small things * Hosted (SaaS) vs Build

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Show Notes  The Data Product Management In Action podcast, brought to you by Soda and executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences.  In Season 01, Episode 18, our host Frannie Helforoush is back again interviewing Katy Pusch about her extensive experience in data product management, particularly with decision-support data products. Katy shares her insights on incorporating machine learning and analytics to empower stakeholders in making informed decisions. They both explore team structure, the challenges encountered in product development, and the critical importance of validating products with users to ensure their effectiveness. About our host Frannie Helforoush: Frannie's journey began as a software engineer and evolved into a strategic product manager. Now, as a data product manager, she leverages her expertise in both fields to create impactful solutions. Frannie thrives on making data accessible and actionable, driving product innovation, and ensuring product thinking is integral to data management. Connect with Frannie on LinkedIn. About our guest Katy Pusch: Katy brings more than a decade of experience in product management and market strategy, driving market change and adoption of innovative technology solutions. She has successfully built and launched data products, IoT solutions, and SaaS platforms in multiple industries such as healthcare, education, and real estate. She is currently serving as a Sr.Product Line Director at Trintech. With a background in research, she brings data science and market intelligence to every aspect of her work. Katy is passionate about data privacy and tech-ethics, and is pursuing an MS in History and Sociology of Technology and Science at GeorgiaTech. When she’s not working with her team to deliver top solutions, Katy enjoys spending time with her husband, building Lego models, and pursuing a private pilot license. Connect with Katy on LinkedIn. All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else.  Join the conversation on LinkedIn.  Apply to be a guest or nominate someone that you know.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights!