Director, Analytics Engineering & BI Platform (Remote Eligible)

The Business Intelligence team exists to unlock the power of data — providing trusted access, actionable insights, and the platforms that power data-driven decisions across the company. We are looking for a seasoned analytics engineering leader to drive the next chapter of our data platform, governance, and intelligence capabilities.

The ideal candidate combines deep technical expertise in modern analytics engineering with a strong command of financial and revenue analytics, and a passion for data quality and governance. This role sits at the intersection of analytics engineering, finance data ownership, and platform strategy — partnering closely with Finance, RevOps, Product, and Engineering to ensure our data assets are trusted, discoverable, and AI-ready. 

You will report to the VP of Data Science and Business Intelligence located in our Bellevue, WA office, or you may work remotely from anywhere in the US where Smartsheet is a registered employer.

You Will:

• 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.
• 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.
• 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.
• 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.
• Own data egress and reverse-ETL strategy, governing pipelines from the data warehouse to downstream platforms including Salesforce, Marketo, Gainsight, Outreach, Thoughtspot, Tableau, and Amplitude.
• 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.
• Lead cross-functional strategic programs including Quote-to-Cash modernization, unified customer data modeling, and the Snowflake-to-Databricks migration, acting as a key decision-maker across multi-quarter initiatives.
• Develop cross-team relationships across functional leadership and BI partners to ensure we are meeting existing analytics needs and are well-positioned to meet the needs of the future.
• Build, develop, and lead a high-performing team, managing vendor and partner relationships, and evolving team capabilities to meet the demands of a rapidly maturing data platform.

You Have:

• 10+ years in analytics engineering, data engineering, or a closely related technical discipline, with 5+ years of people management and a track record of developing senior ICs and leads.
• Deep hands-on proficiency in SQL, Python, and dbt with strong experience on Databricks, familiarity with Snowflake. 
• Proven experience with Finance and Revenue analytics — Bookings, ARR, NDRR, Quote-to-Cash — and demonstrated ability to partner effectively with Finance and RevOps stakeholders.
• Expert-level data modeling skills spanning Data Vault 2.0 (hub/satellite/link design), Medallion architecture (Bronze/Silver/Gold layer design for lakehouse environments), and Semantic Layer development (dbt Semantic Layer,) — with the ability to set modeling standards and make authoritative architectural decisions across all three paradigms. 
• Deep understanding of DataOps and data reliability practices, including CI/CD for data pipelines, automated testing frameworks (dbt tests), and orchestration governance (Airflow), layered data presentations.
• Demonstrated ownership of data governance programs at scale — quality, lineage, cataloging, access management — including data contract design between producers and consumers.
• Experience operating and managing data egress and reverse-ETL pipelines to CRM, marketing automation, and customer success platforms.
• Experience leading cloud data platform migrations (Snowflake, Databricks, or comparable lakehouse architectures) and familiarity with AI/ML-adjacent data requirements including feature stores, embedding-ready models, and the infrastructure needs of LLM and agentic applications.
• Strong program management instincts with the ability to navigate complex, cross-functional initiatives and translate technical complexity into clear narratives for executive and practitioner audiences alike.
• Experience in a SaaS organization with familiarity with microservices-based product architectures, and a conviction that data modeling is software engineering — held to the same standards of testing, documentation, and deployment rigor.