Senior Data Engineer

In this role, you will

• Architect for scale: Develop and monitor robust data pipelines using Python, SQL, and Rundeck/Airflow that handle large-scale data volumes with high reliability.
• Optimize performance: Design performant data models and optimize complex SQL queries for high-scale environments.
• Ensure data reliability: build automated checks and alerts to catch errors early so financial and operational reports remain accurate.
• Increase data access: automate repetitive tasks and build self-service tools to make data easier to use across teams.
• Drive engineering excellence: experiment with and leverage AI-assisted coding tools to accelerate development cycles and enhance code quality.
• Work with partners across the company: translate business questions into practical, maintainable technical solutions.

Please apply if you have

• 5+ years of proven experience in data engineering or analytics engineering within a related data-centric environment.
• Experience owning end-to-end data pipelines (ingest → transform → test → monitor).
• Strong SQL skills and familiarity with distributed/columnar/OLAP systems (examples: Redshift, Trino, Vertica) and relational databases (e.g., MySQL, Postgres).
• Proficiency in Python and a software-engineering approach to building testable, maintainable code.
• Experience with workflow orchestration and containerization (examples: Airflow, Docker).
• Strong analytical and troubleshooting skills for investigating data issues.
• Strong written and spoken English to communicate effectively across teams.

We welcome diverse technical backgrounds. Nice-to-have experience

• Stream processing or real-time systems (e.g., Kafka, Flink).
• Modern BI tooling (e.g., Looker, Superset).
• CI/CD and deployment automation (e.g., GitHub Actions).
• Web frameworks for internal tools (e.g., Django, Flask).