Data Engineer

- Modern tooling: Fluency with Git/GitHub, CI/CD pipelines, and containerized development and deployment - Comfort at scale: Experience with high-volume telemetry, event, IoT, or log data, where the size of the dataset materially changes how you design the pipeline - Cloud data warehousing: Experience with BigQuery or a comparable columnar warehouse (Snowflake, Redshift, Databricks), including the design decisions that keep large tables performant and affordable - Bachelor's degree in Computer Science, Engineering, a quantitative field, or equivalent practical experience - A strong SQL foundation: Able to write, debug, and optimize complex SQL from scratch, and comfortable reasoning about query plans, joins, window functions, and aggregation at scale - Ability to work in a collaborative, fast-paced environment, and the ability to learn and implement new technologies quickly - Excellent problem-solving, analytical, and communication skills, including the ability to explain data caveats to non-technical partners - GCP experience: Working knowledge of Google Cloud, ideally including Cloud Run, Pub/Sub, Cloud Storage, and IAM — or a demonstrated ability to pick up a new cloud quickly - Python-first data engineering: 3+ years of professional experience building production data pipelines, with strong, hands-on Python — this is the core of the role, not a nice-to-have - Ability to effectively leverage AI coding tools — particularly hands-on experience with Claude Code, including…