Director, Analytics Engineering and AI

Pluralsight

3d•$167,200 - $220,000•Hybrid

About The Position

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.

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.

Nice To Haves

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.

Responsibilities

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.