Strategies to position yourself for success in the data & AI job market, including resumes and interview approaches.

Exploration of the different career paths in data & AI and what each path entails.

Discussion on approaching careers in data & AI, exploring career thinking frameworks and practical guidance for navigating the evolving job market.

In this talk, Xia He-Bleinagel, Head of Data & Cloud at NOW GmbH, shares her remarkable journey from studying automotive engineering across Europe to leading modern data, cloud, and engineering teams in Germany. We dive into her transition from hands-on engineering to leadership, how she balanced family with career growth, and what it really takes to succeed in today’s cloud, data, and AI job market.

TIMECODES: 00:00 Studying Automotive Engineering Across Europe 08:15 How Andrew Ng Sparked a Machine Learning Journey 11:45 Import–Export Work as an Unexpected Career Boos t17:05 Balancing Family Life with Data Engineering Studies 20:50 From Data Engineer to Head of Data & Cloud 27:46 Building Data Teams & Tackling Tech Debt 30:56 Learning Leadership Through Coaching & Observation 34:17 Management vs. IC: Finding Your Best Fit 38:52 Boosting Developer Productivity with AI Tools 42:47 Succeeding in Germany’s Competitive Data Job Market 46:03 Fast-Track Your Cloud & Data Career 50:03 Mentorship & Supporting Working Moms in Tech 53:03 Cultural & Economic Factors Shaping Women’s Careers 57:13 Top Networking Groups for Women in Data 1:00:13 Turning Domain Expertise into a Data Career Advantage

Connect with Xia- Linkedin - https://www.linkedin.com/in/xia-he-bleinagel-51773585/ - Github - https://github.com/Data-Think-2021 - Website - https://datathinker.de/

Connect with DataTalks.Club: - Join the community - https://datatalks.club/slack.html - Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ - Check other upcoming events - https://lu.ma/dtc-events - GitHub: https://github.com/DataTalksClub - LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

In this talk, Anusha Akkina, co-founder of Auralytix, shares her journey from working as a Chartered Accountant and Auditor at Deloitte to building an AI-powered finance intelligence platform designed to augment, not replace, human decision-making. Together with host Alexey from DataTalks.Club, she explores how AI is transforming finance operations beyond spreadsheets—from tackling ERP limitations to creating real-time insights that drive strategic business outcomes.

TIMECODES: 00:00 Building trust in AI finance and introducing Auralytix 02:22 From accounting roots to auditing at Deloitte and Paraxel 08:20 Moving to Germany and pivoting into corporate finance 11:50 The data struggle in strategic finance and the need for change 13:23 How Auralytix was born: bridging AI and financial compliance 17:15 Why ERP systems fail finance teams and how spreadsheets fill the gap 24:31 The real cost of ERP rigidity and lessons from failed transformations 29:10 The hidden risks of spreadsheet dependency and knowledge loss 37:30 Experimenting with ChatGPT and coding the first AI finance prototype 43:34 Identifying finance’s biggest pain points through user research 47:24 Empowering finance teams with AI-driven, real-time decision insights 50:59 Developing an entrepreneurial mindset through strategy and learning 54:31 Essential resources and finding the right AI co-founder

Connect with Anusha - Linkedin - https://www.linkedin.com/in/anusha-akkina-acma-cgma-56154547/ - Website - https://aurelytix.com/

Connect with DataTalks.Club: - Join the community - https://datatalks.club/slack.html - Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ - Check other upcoming events - https://lu.ma/dtc-events - GitHub: https://github.com/DataTalksClub - LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

At Qdrant Conference, builders, researchers, and industry practitioners shared how vector search, retrieval infrastructure, and LLM-driven workflows are evolving across developer tooling, AI platforms, analytics teams, and modern search research.

Andrey Vasnetsov (Qdrant) explained how Qdrant was born from the need to combine database-style querying with vector similarity search—something he first built during the COVID lockdowns. He highlighted how vector search has shifted from an ML specialty to a standard developer tool and why hosting an in-person conference matters for gathering honest, real-time feedback from the growing community.

Slava Dubrov (HubSpot) described how his team uses Qdrant to power AI Signals, a platform for embeddings, similarity search, and contextual recommendations that support HubSpot’s AI agents. He shared practical use cases like look-alike company search, reflected on evaluating agentic frameworks, and offered career advice for engineers moving toward technical leadership.

Marina Ariamnova (SumUp) presented her internally built LLM analytics assistant that turns natural-language questions into SQL, executes queries, and returns clean summaries—cutting request times from days to minutes. She discussed balancing analytics and engineering work, learning through real projects, and how LLM tools help analysts scale routine workflows without replacing human expertise.

Evgeniya (Jenny) Sukhodolskaya (Qdrant) discussed the multi-disciplinary nature of DevRel and her focus on retrieval research. She shared her work on sparse neural retrieval, relevance feedback, and hybrid search models that blend lexical precision with semantic understanding—contributing methods like Mini-COIL and shaping Qdrant’s search quality roadmap through end-to-end experimentation and community education.

Speakers

Andrey Vasnetsov Co-founder & CTO of Qdrant, leading the engineering and platform vision behind a developer-focused vector database and vector-native infrastructure. Connect: https://www.linkedin.com/in/andrey-vasnetsov-75268897/

Slava Dubrov Technical Lead at HubSpot working on AI Signals—embedding models, similarity search, and context systems for AI agents. Connect: https://www.linkedin.com/in/slavadubrov/

Marina Ariamnova Data Lead at SumUp, managing analytics and financial data workflows while prototyping LLM tools that automate routine analysis. Connect: https://www.linkedin.com/in/marina-ariamnova/

Evgeniya (Jenny) Sukhodolskaya Developer Relations Engineer at Qdrant specializing in retrieval research, sparse neural methods, and educational ML content. Connect: https://www.linkedin.com/in/evgeniya-sukhodolskaya/

Como é construir a próxima geração de sistemas inteligentes em uma das maiores operações de beleza do mundo — conectando engenharia de dados, IA generativa e agentes de IA em um ecossistema que já nasce preparado para o futuro? Neste episódio, conversamos com Felipe Gusmão Contratres, Marcel “Xiquin” Cecchin e Yasmim Vasconcelos, do Grupo Boticário, sobre como a evolução da engenharia de dados está permitindo a criação de AI Agents que vão muito além da análise. A conversa parte de uma pergunta essencial:como empresas que já dominam a IA generativa estão dando o próximo passo, habilitando agentes inteligentes a partir de uma nova infraestrutura de dados ? Falamos sobre a transformação da engenharia de dados, que deixou de focar apenas em relatórios e passou a operar como a fundação para sistemas avançados. Exploramos também como tecnologias que estão sendo utilizadas dentro do Grupo Boticário para habilitar produtos reais baseados em IA.

Nossa Bancada Data Hackers: Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart.Gabriel Lages — Co-founder da Data Hacker e Diretor de Dados & AI da Hotmart

Strategies to position yourself effectively for roles in Data & AI, including resume, interviewing, and personal branding.

Overview of different career paths in data & AI and how to navigate transitions and growth.

Overview of how AI and data are transforming careers, the evolving job market, and practical strategies to land interviews.

Deliver measurable business value by applying strategic, technical, and ethical frameworks to AI initiatives at scale Free with your book: DRM-free PDF version + access to Packt's next-gen Reader Key Features Build AI strategies that align with business goals and maximize ROI Implement enterprise-ready frameworks for MLOps, LLMOps, and Responsible AI Learn from real-world case studies spanning industries and AI maturity levels Book Description AI is only as valuable as the business outcomes it enables, and this hands-on guide shows you how to make that happen. Whether you’re a technology leader launching your first AI use case or scaling production systems, you need a clear path from innovation to impact. That means aligning your AI initiatives with enterprise strategy, operational readiness, and responsible practices, and The AI Optimization Playbook gives you the clarity, structure, and insight you need to succeed. Through actionable guidance and real-world examples, you’ll learn how to build high-impact AI strategies, evaluate projects based on ROI, secure executive sponsorship, and transition prototypes into production-grade systems. You’ll also explore MLOps and LLMOps practices that ensure scalability, reliability, and governance across the AI lifecycle. But deployment is just the beginning. This book goes further to address the crucial need for Responsible AI through frameworks, compliance strategies, and transparency techniques. Written by AI experts and industry leaders, this playbook combines technical fluency with strategic perspective to bridge the business–technology divide so you can confidently lead AI transformation across the enterprise. Email sign-up and proof of purchase required What you will learn Design business-aligned AI strategies Select and prioritize AI projects with the highest potential ROI Develop reliable prototypes and scale them using MLOps pipelines Integrate explainability, fairness, and compliance into AI systems Apply LLMOps practices to deploy and maintain generative AI models Build AI agents that support autonomous decision-making at scale Navigate evolving AI regulations with actionable compliance frameworks Build a future-ready, ethically grounded AI organization Who this book is for This book is for AI/ML leaders and business leaders, CTOs, CIOs, CDAOs, and CAIOs, responsible for driving innovation, operational efficiency, and risk mitigation through artificial intelligence. You should have familiarity with enterprise technology and the fundamentals of AI solution development.

In the era of information overload, organizations struggle to harness the vast amount of unstructured data stored across presentations, reports, images, and text documents. That's why we created the "Autocurator", an AI-powered tool designed to automatically extract, structure, and curate knowledge from heterogeneous document repositories to support Retrieval-Augmented Generation (RAG) systems. Autocurator integrates advanced document parsing pipelines, multimodal AI models, and semantic structuring techniques to convert diverse content - including text, slides, tables, and diagrams - into machine-readable knowledge. This enables downstream RAG systems to query not only text-based insights but also visual and conceptual knowledge that traditionally remained inaccessible. Our system employs a multi-stage pipeline: (1) document ingestion and format normalization, (2) de-duplication of redundant and conflicting information (3) multimodal content understanding using large language and vision models, (4) entity and relationship extraction with human-in-the-loop validation, and (5) generation of structured outputs optimized for retrieval. We will showcase Autocurator’s effectiveness on large enterprise document corpora, showcasing significant gains in retrieval precision and generative quality across several applied AI use cases. By bridging unstructured data and structured knowledge, Autocurator provides a scalable and transparent foundation for next-generation knowledge management and reasoning systems.

Cloud gave us access to infinite compute power. AI is giving us access to infinite output. But both are leaving us a bit bewildered - how do we navigate our way into getting value from this world of infinite possibilities at our fingertips? What skills do we need and where the heck do we start?

What happens when a love of gardens meets the possibilities of AI? Lyn Callender-Easby, founder of DearGarden, shares how curiosity, empathy, and one wildly over-engineered PowerPoint garden plan grew into an AI-enabled garden companion helping people design and care for gardens they love.

An "eval," short for evaluation, is a set of structured tests or benchmarks used to systematically assess the performance and quality of an AI model or a program. Creating evals is a foundational practice when building solutions with AI. In this talk Amy Heineike will provide an introduction to evals: What are they? Why do you need them? And how do you get started?

How to build the worlds fastest AI gateway and why enables your application builders to move faster

The future of AI isn't just about bigger models, it's about smarter deployment. Discover how agentic AI systems and federated small models are revolutionising privacy, performance and user experience, by operating directly on devices rather than relying solely on centralised infrastructure.

Transformer la donnée en valeur concrète (et éviter l'IA gadget)

Overview of the context problem: why generic AI process advice falls short; the importance of enterprise-specific context and relationships; preview of possibilities when AI truly understands your business.

Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conheça nossos comentaristas do Data Hackers News: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠Preencha a pesquisa State of Data Brazil: https://www.stateofdata.com.br/ Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠