Eliminate 80% of the manual effort spent writing dbt tests, chasing noisy alerts, and fixing data issues. In this session, you'll see how data teams are using an AI Data SRE that detects, triages, and resolves issues across the entire data stack. We’ll cover everything from AI architecture to optimised incident management–and even show an agent writing production-ready PRs!

Tristan talks with Mikkel Dengsøe, co-founder at SYNQ, to break down what agentic coding looks like in analytics engineering. Mikkel walks through a hands-on project using Cursor, the dbt MCP server, Omni's AI assistant, and Snowflake. They cover where agents shine (staging, unit tests, lineage-aware checks), where they're risky (BI chat for non-experts), and how observability is shifting from dashboards to root-cause explanations. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

With data teams' growing ambition to build business automation, AI systems, or customer-facing products, we must shift our mindset about data quality. Mechanically applied testing will not be enough; we need a more robust strategy similar to software engineering. In this talk, I outline a new approach to data testing and observability anchored in the ‘Data Products’ concept and walk through the practical implementation of a production-grade analytics system with dbt as the backbone. The learnings will apply to data practitioners using dbt whether they're just getting started or working in a large enterprise.

Lunar, a leading Nordic digital bank, successfully implemented a data governance framework to enhance data quality and secure C-level buy-in by using SYNQ, a data reliability and observability tool. 

Their framework focuses on data ownership, criticality, and monitoring. Lunar's data team, leveraging tools like SYNQ, ensures high standards against financial crime, personalisation through AI, and reliable reporting. 

They maintain oversight through automated monitoring, use of data products, and a robust ownership model, which enhances data quality and accelerates issue resolution for their reports to executives. 

This approach enables Lunar’s data engineering and data governance teams to work in harmony, and operate efficiently without having to increase headcount.