Director, Analytics Engineering & Bi Platform (remote Eligible)

Smartsheet Smartsheet · Seattle · United States · Business Intelligence & Ops

Smartsheet is seeking an Analytics Engineering leader to drive their data platform, governance, and intelligence capabilities. This role will focus on modern analytics engineering practices, financial analytics, and data governance, with a significant emphasis on preparing data assets to be AI-ready for AI agents and LLM-based analytics. The candidate will lead strategy, own the semantic layer, drive finance analytics, set data governance standards, manage data egress, and shape the AI data strategy, reporting to the VP of Data Science and Business Intelligence.

What you'd actually do

  1. Lead analytics engineering strategy and execution, owning the design and evolution of core data models using modern practices — Data Vault, dimensional modeling, dbt on Snowflake — with a clear roadmap toward a Databricks lakehouse architecture.
  2. Own the company-wide semantic layer and metrics store, ensuring Bookings, ARR, NDRR, and other critical business metrics have a single, version-controlled, trusted definition consumable by every downstream tool and AI agent.
  3. Drive Finance Analytics, including ownership of Bookings, ARR, NDRR, segment and territory reporting, and month-end close pipelines, partnering closely with Finance and Revenue Operations.
  4. Set the standard for data governance and data quality, including discoverability, lineage, access controls, and data contracts between upstream producers and downstream consumers — leveraging Atlan, Unity Catalog, and Monte Carlo.
  5. Shape our AI data strategy, ensuring data assets are structured, documented, and governed to serve as reliable foundations for AI agents, LLM-based analytics, and intelligent product features — while driving data-as-a-product principles and platform cost discipline.

Skills

Required

  • SQL
  • Python
  • dbt
  • Databricks
  • Snowflake
  • Data Vault 2.0
  • Medallion architecture
  • Semantic Layer development
  • DataOps
  • CI/CD for data pipelines
  • Automated testing frameworks
  • Airflow
  • Data governance
  • Data lineage
  • Data cataloging
  • Access management
  • Data contracts
  • Reverse-ETL
  • Cloud data platform migrations
  • Feature stores
  • Embedding-ready models
  • LLM infrastructure
  • Agentic applications
  • Program management
  • SaaS organizations

Nice to have

  • Atlan
  • Unity Catalog
  • Monte Carlo
  • Salesforce
  • Marketo
  • Gainsight
  • Outreach
  • Thoughtspot
  • Tableau
  • Amplitude
  • Quote-to-Cash modernization
  • Unified customer data modeling
  • Snowflake-to-Databricks migration

What the JD emphasized

  • 10+ years in analytics engineering, data engineering, or a closely related technical discipline, with 5+ years of people management
  • Deep hands-on proficiency in SQL, Python, and dbt
  • Proven experience with Finance and Revenue analytics
  • Expert-level data modeling skills spanning Data Vault 2.0, Medallion architecture, and Semantic Layer development
  • Deep understanding of DataOps and data reliability practices
  • Demonstrated ownership of data governance programs at scale
  • Experience operating and managing data egress and reverse-ETL pipelines
  • Experience leading cloud data platform migrations
  • Strong program management instincts
  • Experience in a SaaS organization

Other signals

  • Shape our AI data strategy
  • data assets are structured, documented, and governed to serve as reliable foundations for AI agents, LLM-based analytics, and intelligent product features
  • driving data-as-a-product principles and platform cost discipline
  • familiarity with AI/ML-adjacent data requirements including feature stores, embedding-ready models, and the infrastructure needs of LLM and agentic applications