Senior Analytics Engineer

Responsibilities

• Designs and builds Unity Catalog metric views as the canonical, versioned definitions of business metrics, with ownership assigned, lineage tracked, and access controls enforced.
• Designs Gold-layer tables and Silver-to-Gold promotion logic in partnership with Data Engineering, ensuring the semantic layer is architecturally sound, performant, and maintainable as the platform grows.
• Migrates legacy consumption logic, including EDW views, legacy data products, and ad-hoc SQL derivation, into governed metric views with validated output parity.
• Sets and enforces standards for metric view design, naming conventions, versioning, and documentation, in alignment with Data Governance metadata standards.
• Builds reusable data products in Unity Catalog, catalogued with owner, classification, and lineage, enabling self-service discovery without ad-hoc engineering intervention.
• Partners with Analytics & BI to ensure metric views meet DirectLake consumption requirements, so dashboards and reports are built on governed definitions rather than re-derived logic.
• Partners with Partner Data Services on metric views serving external partner feeds, ensuring feed-facing definitions meet contract schema and quality requirements.
• Documents metric definitions, promotion logic, and data products to team standards, and contributes plain-language definitions to the business glossary, ensuring no semantic layer asset depends on a single point of failure.
• Implements validation and observability for semantic layer assets, ensuring metric changes are versioned, tested, and communicated before downstream consumers are affected.
• Serves as the senior escalation point for metric definition conflicts, working with business stakeholders and Data Governance to resolve competing definitions into a single canonical version.
• Mentors Analytics Engineers on semantic layer design, SQL and PySpark craft, and documentation discipline; reviews pull requests in Azure DevOps & Git to maintain code quality.
• Serves as a technical liaison with business stakeholders, translating business metric requirements into precise, testable definitions and explaining definition decisions in plain language.
• Collaborates with Data Engineering on platform architecture decisions affecting the semantic layer, and with Data Governance on metadata, classification, and lineage standards.

Qualifications

• 6+ years of progressive analytics engineering or data engineering experience, including production ownership of a semantic layer, metrics layer, or governed consumption layer serving multiple downstream consumers.
• Deep, hands-on expertise with Databricks in production environments: Unity Catalog, Delta Lake, medallion architecture, and Gold-layer design.
• Demonstrated experience establishing canonical metric definitions for reuse: defining a business metric once and serving it consistently to BI, analytics, and external consumers.
• Advanced SQL and strong PySpark and Python proficiency, sufficient to design, build, and optimize complex promotion logic and metric definitions independently.
• Strong dimensional and consumption-oriented data modeling skills: designing Gold-layer structures for query performance, reusability, and downstream consumption.
• A clear understanding of analytics engineering as a discipline distinct from data engineering (pipelines, ingestion) and BI (dashboards, reports), and the ability to operate at that seam.
• Strong communication skills; able to work directly with business stakeholders, Governance, QA, and Informatics to converge on precise metric definitions.