Director Analytics AI

Job Description: The Director, Analytics Engineering and AI leads the data engineering group and owns the curated, certified data foundation the business relies on to make decisions. The leader is accountable for a cohesive, coordinated data model covering both product and enterprise data. They also build the certified semantic layer that makes this foundation reliably and safely accessible to AI. They lead a team of senior and principal engineers through a significant platform transition towards AI while sustaining the revenue-, pipeline-, and finance-critical reporting the company depends on every day. This is a hands-on, player-coach role: the right leader sets direction and grows the team, but also rolls up their sleeves — modeling data, writing and reviewing SQL and dbt code leveraging AI, and getting into the weeds to solve the hardest problems alongside their engineers. What you'll do: - Lead, develop, and grow the Analytics Engineering team — setting technical direction, standards, and priorities — while sustaining business-critical reporting (revenue, product, marketing, finance) without interruption. - Own a single, unified data transformation and model spanning both product/behavioral and enterprise (GTM, finance) data. - Establish and expand the certified contextual layer that ensures reliable and safe access to the data warehouse for analytics. This layer builds on a governed data dictionary and metrics store. It also covers lineage, freshness, ownership, access, entity relationships, and business knowledge. - Drive the migration of transformation logic out of integration/middleware tooling into governed, certified models owned by the team. - Partner across Data Analytics, Data Engineering, Data Architecture, Data Governance to plan and execute cross-team initiatives. - Serve as a senior partner to collaborators across Revenue, Marketing, Finance, GTM, and Success. Translate business needs into scalable, balanced analytics solutions. Communicate mentorship, compromises, and outcomes to senior leadership. - Set and uphold engineering standards — modeling conventions, certification practices, code quality, and documentation. - Stay hands-on in the work: build and review data models, write and debug SQL and dbt code leveraging AI, dig into data-quality issues, and take direct ownership of the most complex or highest-stakes problems. Experience you'll bring: - Familiarity with high-performance OLAP serving layers (e.g., ClickHouse) for product- or customer-facing analytics. - Experience reducing reliance on integration/middleware tooling by migrating transformation logic into governed models. - Experience partnering with a data governance function on company-wide standards, schema/version control, and PII classification. Requirements: - Requires a minimum of 12 years of related or equivalent experience; or 8+ years and an advanced degree. - Demonstrated hands-on expertise in data curation, transformation, and dimensional/medallion data modeling on a modern cloud data stack (e.g., Snowflake, dbt). - Proven breadth of data modeling across both product/behavioral event data and enterprise GTM/finance data. - Current, hands-on technical depth in making productive use of AI. This is a player-coach role, not a purely managerial one. - Experience leading a team of senior and principal engineers through a significant platform or architecture transition — not only steady-state delivery, but change. - Strong grasp of data warehousing architecture, data governance. - Ability to clearly communicate direction, trade-offs, and results to senior, non-technical leaders as well as technical team members. - Experience building a semantic/contextual layer and making data AI-ready — including patterns such as retrieval-augmented generation (RAG) or natural-language analytics over a governed warehouse.