Principal Analytics Engineer

Angi•Denver, CO

1d•Remote

About The Position

The Principal Analytics Engineer for Product Analytics will shape how product data is transformed and consumed, ensuring that both human analysts and AI agents receive consistent metrics, faster iteration, and trustworthy answers. This role is central to designing and evolving an analytics architecture that radically shortens the distance from complex business questions to validated insights. As a Principal leader, you will be the connective tissue across data engineering, analytics, and product teams—architecting pipelines, semantic layers, and quality practices so they work as a singular, cohesive system. The ideal candidate is a visionary technical simplifier with deep expertise in modern data stacks, a passion for developer/analyst velocity, and a proven ability to enable partner teams. You will play a dual role: empowering human analysts to spend less time debugging and more time driving strategy, while simultaneously hardening our data layer into a load-bearing, semantic infrastructure that AI tools can query accurately and safely.

Requirements

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.

Nice To Haves

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.

Responsibilities

Design, build, and evolve high-scale dbt models that transform raw upstream inputs into clean, well-documented, analytics-ready data products with clear contracts and ownership.
Implement critical guardrails, clear grain definitions, meaningful metadata descriptions, and approved access paths so AI tools and agents can query data accurately without bypassing governance.
Shape how metrics are defined and exposed so that analysts, dashboards, and AI tools all return the same answer to the same question, turning the semantic layer into production-grade infrastructure for AI-powered BI.
Translate recurring analyst pain points—such as ambiguous metrics, broken joins, or undocumented fields—into durable, reusable models, patterns, and shared interfaces.
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.
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.
Collaborate with data and analytics engineering peers to establish and maintain global patterns, package management, and repository best practices.
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.