Cyril Sonnefraud

Meet Sam. A year ago she built data pipelines. Today she runs a data team she didn't hire. A load fails at 2am; AI Agents investigate and leave a fix for her approval by 9am. The migration nobody wanted to own moves through in the background, while Sam works the "needs attention" items. Once work arrives faster than one person can check it, the constraint stops being what agents can do and starts being how far you trust them. This is a day in the life of the agentic data team, and it changes what "data engineer," "manager," and "data leader" each mean. Join Matillion's Julian Wiffen and Cyril Sonnefraud to see how continuous context and autonomous execution turn every contributor into a team lead — and why that's the most important org change data leaders will navigate this decade.

It's 8:47 a.m. Monday. Your analyst Slacks, "Can we get active-customer churn by tier for the board deck at 10?" In most data teams, that’s a sprint ticket. For a team running Maia on Snowflake, it’s live by 9:30 a.m. This session walks through a day in the life of a data engineer who moves from request handler to operator. Maia reads Snowflake schemas, dbt models and team context, so “active customer” means what finance means. It handles urgent requests, clears Jira backlog, converts PowerCenter to Snowflake-native ELT, and hands governed data products to Cortex and Snowflake CoWork. Live build, proven at Cisco, Docusign and Mercedes F1.

Data engineering backlogs are the #1 constraint on AI progress. Teams burn out while demand outpaces hiring. This session explores how agentic AI like Maia introduces a new model for scaling data teams—autonomous agents that plan, build, and maintain pipelines end-to-end, shifting teams from reactive ticket fulfillment to proactive AI enablement.

This session will explore the evolving role of data engineers. Data engineering is currently a bottleneck due to overwhelming requests and complex knowledge work. Maia acts as a "digital data engineer" or a "virtual data team" that amplifies productivity by 100x. It enables users, from skilled engineers to citizen data analysts, to author pipelines in natural business language. The session will demonstrate Maia's ability to accelerate mundane and advanced tasks,troubleshoot and debug pipelines in real-time, and generate high-quality, auditable pipelines using Matillion's proprietary, human-readable Data Pipeline Language (DPL), which avoids "spaghetti code" common with generic LLMs.

This session will provide a Maia demo with roadmap teasers. The demo will showcase Maia's core capabilities: authoring pipelines in business language, multiplying productivity by accelerating tasks, and enabling self-service. It demonstrates how Maia takes natural language prompts and translates them into YAML-based, human-readable Data Pipeline Language (DPL), generating graphical pipelines. Expect to see Maia interacting with Snowflake metadata to sample data and suggest transformations, as well as its ability to troubleshoot and debug pipelines in real-time. The session will also cover how Maia can create custom connectors from REST API documentation in seconds, a task that traditionally takes days . Roadmap teasers will likely include the upcoming Semantic Layer, a Pipeline Reviewing Agent, and enhanced file type support for various legacy ETL tools and code conversions.