Data Engineer

- A systems thinker who reasons carefully about freshness, correctness, and failure modes, especially for data that everything else depends on - Experience running data systems in production: monitoring, alerting, debugging, incident response habits, and pragmatic SLO/SLA thinking - Deep experience with Snowflake, dbt, and an orchestrator such as Prefect, Airflow, or Dagster, and with ingesting data from production databases and SaaS sources, including backfill and reprocessing patterns - Experience with warehouse access governance: RBAC, masking policies, and PII handling - Working knowledge of how GTM, Finance, and Product teams operate in addition to working with Engineering, and experience translating ambiguous business questions into technical specs - Proven experience operating as the only engineer on a layer, balancing foundational architecture with urgent business needs - Strong SQL and Python; disciplined engineering practices (tests, docs, reviews, CI/CD) - Comfortable using LLMs and coding agents as part of your day-to-day development workflow, with the judgment to validate their output and scale it into shared processes, and embracing AI and automation to scale platform work and accelerate your teammates - Strong data modeling judgment and an understanding of schema evolution - 5+ years (or equivalent) building and operating production data platforms, including the transformation layer - Pragmatic: you start with the simplest solution that works, prove it, then…