Senior Analytics Engineer
Data platforms · Pipelines at scale · AI-augmented workflows
I've been around data systems long enough to have seen a few hype cycles. Started on the infrastructure and support side — ERPs, system monitoring, the unglamorous stuff — which gave me a grounding that most data people don't have. Moved into engineering from there: pipelines, dashboards, ETL work across APIs and databases.
For the last few years I've been doing analytics engineering at scale — end-to-end, from stakeholder alignment and data modeling to delivery, monitoring and governance across multiple business domains. The job is mostly a translation problem: between what the business actually needs and what the platform can reliably do.
Lately the focus has shifted toward AI tooling. Not the hype side — the engineering side: scaffolding, evals, harness workflows. The kind of work that makes AI a dependable part of how a data team actually operates.
