Director Analytics Engineering

Pluralsight is a company dedicated to accelerating the technology skills of the workforce. The Director of Analytics Engineering and AI will lead the data engineering group, ensuring a cohesive data model and overseeing the transition towards AI while maintaining critical reporting for the business.

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

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