Tomas Peluritis
Tomas leads data at Mediatech and runs Uncle Data, a newsletter and podcast for data engineers who prefer practical advice over hype. By day, he manages pipelines processing half a billion events; by night, he writes about what he learned (often the hard way). When not wrangling DAGs or mentoring his team, he's probably optimising a Magic: The Gathering deck.
Sessions
Every team has code that makes them cringe. Legacy pipelines nobody dares touch, "temporary" tables that became the source of truth, and SQL queries that outlived three team leads. We call it technical debt, and the instinct is always the same: fix it.
But should we? After migrating from Redshift to Snowflake, learning PIG just to rewrite a pipeline deprecated months later, and surviving Airflow OOM kills, I have opinions. And maybe a framework slightly better than "it depends."
Building great engineering teams has never been straightforward — but the rules keep changing. Three back-to-back panel discussions with CTOs and engineering leaders covering the full spectrum: scaling teams and competing for talent in a small market, navigating a leadership role that looks nothing like it did five years ago (AI included), and making the hard calls on culture, tech debt, and architecture when there's no clear playbook to follow.
Airflow basics are well documented. Production Airflow is not. This talk covers the patterns, costs, and migration pitfalls that only show up after you've deployed: dynamic DAGs that scale, sensors that don't waste resources, CloudWatch bills that surprise you, and MWAA version upgrades that break in ways the changelog didn't mention. Practical lessons for teams running Airflow beyond the tutorial stage.