Principal Analytics Engineer

Friction Elimination: Translate recurring analyst pain points—such as ambiguous metrics, broken joins, or undocumented fields—into durable, reusable models, patterns, and shared interfaces.

Quality & Governance Standards: Set and hold the bar for what "done" means for a data product, embedding robust testing, freshness expectations, data catalogs, and metadata ownership into the development lifecycle.

Cross-Functional Alignment: Coordinate across data platforms and product teams to map use cases, eliminate duplicate work, surface technical tradeoffs early, and drastically reduce cycle times from business question to trusted answer.

Shared Frameworks: Collaborate with data and analytics engineering peers to establish and maintain global patterns, package management, and repository best practices.

Mentorship & Review: Raise the collective engineering bar across the organization by running technical reviews, hosting office hours, and leading pair-programming sessions with domain analysts and engineers.

Minimum Qualifications

12+ years of experience in analytics engineering, data engineering with heavy analytics partnership, or an equivalent technical data role.

Expert-level SQL and strong Python proficiency for advanced data work, with a proven track record of delivering on a modern data stack (transformation-as-code, orchestration, warehouses, and BI).

Experience with data governance tooling, including data catalogs, lineage systems, and policy-aware access patterns.

Exceptional cross-functional leadership skills with a history of influencing engineering and product teams without direct authority.

Strong technical communication skills, with the unique ability to explain complex modeling decisions to a product analyst without jargon, and to a platform engineer without oversimplifying.

Preferred Qualifications

Hands-on experience working directly with dbt and Snowflake in a high-scale environment.

Practical experience implementing data mesh architectures or federated data ownership models.

Exposure to AI/LLM tooling as a data consumer, with a strong conceptual grasp of what clean, well-structured data requires to be successfully queried by AI agents and assistants.

We know that the best ideas come from teams where diverse points of view uncover new solutions to hard problems. We welcome and value individuals who bring diverse life experiences, educational backgrounds, cultures, and work experiences.

Our hiring process may utilize artificial intelligence (AI) tools to assist in candidate screening and assessment. Our AI tools are designed to complement, not replace, human decision-making.

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