Own end-to-end reliability for core data pipelines—from ingestion through billing and reporting outputs.
Build observability foundations: health dashboards, tiered alerting and on-call signals that are actionable, not noisy.
Harden data ingestion with typed staging, quarantine patterns, freshness gates and safe backfill and replay capabilities.
Enforce schema contracts and quality validation so failures are caught in CI, not discovered by stakeholders.
Establish clear boundaries between governed core data and downstream BI and reporting layers.
Lead incident retrospectives that fix root causes, not just symptoms, and reduce future on-call burden.
Mentor 2–4 data engineers through code reviews, pairing and growth conversations.

Bachelors Degree at a minimum.
7+ years in data engineering, with meaningful ownership of production pipelines and quality systems.
Deep dbt experience—you've maintained real deployments, not just prototypes.
Strong SQL and data modeling instincts, with an eye for performance and cost.
Python fluency for tooling, validation and automation.
Hands-on experience with data quality monitoring: contracts, freshness checks, schema enforcement and alerting.
Have led or mentored small engineering teams, formally or informally.
You think in systems—and you fix root causes, not just symptoms.

Lead Data Engineer, Data Platform

Key skills