Manager, Analytics Engineering

Responsibilities

• Owns the Unity Catalog semantic layer as the canonical, governed source of truth for all business metrics: metric view definitions, Silver-to-Gold promotion logic, data mart architecture, and data product development.
• Establishes the principle that every business metric is defined once as a metric view and made available, consistently and correctly, to every downstream consumer: Power BI dashboards via DirectLake, external partner feeds, internal analytics, and AI model inputs.
• Sets and enforces standards for metric view design, naming conventions, versioning, and documentation, in alignment with Data Governance metadata standards and Unity Catalog access controls.
• Pairs with the Data Architecture on Gold-layer design decisions, ensuring the semantic layer is architecturally sound, maintainable, and scalable as the platform grows.
• Led the development of reusable data products in Unity Catalog, building the data product catalog that enables self-service analytics discovery without ad-hoc engineering intervention.
• Ensures analytics requests enter the engineering pipeline with defined acceptance criteria and data product specifications before build work begins, in coordination with Informatics intake.
• Drives consistency and reuse across analytics delivery by replacing bespoke, one-off SQL derivation with governed, versioned metric views and data products.
• Develops a team culture oriented around the semantic layer discipline, which is distinct from both Data Engineering (pipeline and platform focused) and Analytics & BI (dashboard and report focused).
• Builds individual development plans and career frameworks for the team, defining clear growth paths within the Analytics Engineering track.
• Fosters a culture of standards adherence, documentation discipline, and reusability across all data product and metric view development.
• Pairs with Analytics & BI to ensure metric views meet BI consumption requirements and that the Analytics & BI team builds on the semantic layer without re-deriving logic.
• Pairs with Data Engineering to ensure Gold-layer tables and Silver-to-Gold promotion logic are aligned with the data engineering platform architecture.
• Pairs with Data Governance to ensure metric view definitions are governed assets: ownership assigned, lineage tracked, and metadata standards enforced.
• Pairs with Informatics to ensure analytics requests enter the build pipeline with defined scope and acceptance criteria.
• Pairs with QA to support data layer validation of analytics outputs against governance-defined quality dimensions.

Qualifications

• 7+ years of progressive data engineering or analytics engineering experience, with at least 2 years in a people management or team lead capacity.
• Deep, hands-on expertise with Databricks: Unity Catalog, metric views, Delta Lake, medallion architecture, and Silver-to-Gold promotion logic in production environments.
• Demonstrated experience owning or building a semantic layer function: metric definitions, data product development, and the discipline of defining business metrics once for reuse across consumers.
• Clear understanding of analytics engineering as a distinct discipline from data engineering (pipelines, ingestion) and BI (dashboards, reports); able to articulate and hire to that distinction.
• Proficiency in SQL and PySpark; sufficient to set engineering standards, review code quality, and make architecture decisions for the semantic layer.
• Proven ability to build and lead teams, including establishing standards and practices in a function without an existing organizational identity to inherit.