Staff Analytics Engineer, Databricks

This is a fully remote position, open to applicants in United States.

📋 Description

• Take charge of Gold-layer design and implementation, which includes facts, dimensions, domain data marts, and regulated KPI executions on Databricks.

• Develop and sustain the enterprise semantic layer through curated views, governed semantic models, metric definitions, and reusable patterns aimed at reliable business use.

• Convert approved KPI and metric definitions into accurate, testable, and auditable transformation logic that aligns with the agreed business context.

• Establish and uphold the promotion pathway from domain Gold to enterprise Gold, ensuring shared metrics are not published without necessary business and governance approval.

• Collaborate directly with business stakeholders across various domains to clarify KPI definitions, address ambiguities, and resolve conflicting definitions prior to implementation.

• Empower domain teams by creating modeling standards, reusable design patterns, review processes, and coaching methods instead of acting as the long-term owner of every downstream use case.

• Evaluate Gold-layer models produced by other teams or partners for accuracy, definition integrity, usability, and compliance with enterprise standards.

• Enhance Gold-layer structures for BI and self-service analytics while maintaining traceability, governance, and metric consistency.

• Ensure documentation and discoverability of semantic models, tables, columns, ownership, and business definitions.

• Incorporate awareness of data sensitivity, classification, and approved usage when designing joins, dimensions, semantic views, and access patterns to ensure the semantic layer reflects both business meaning and compliance requirements.

⛳️ Requirements

• Over 8 years of experience in analytics engineering, BI engineering, or data engineering with a strong foundation in dimensional modeling.

• Practical experience with Databricks, including Delta Lake and Spark SQL and/or PySpark, along with a solid understanding of semantic-layer concepts.

• Proven experience in translating ambiguous business KPI requests into clear and auditable transformation logic.

• Strong SQL and data modeling expertise, encompassing star schemas, fact and dimension design, and slowly changing dimensions.

• Capability to engage directly with business stakeholders and act as a robust technical counterpart on metric definitions and model quality.

• Demonstrated understanding of the underlying business processes and data domains related to the metrics being modeled, beyond merely implementing requested transformations.

• Ability to assess whether data should be exposed in a semantic layer based on sensitivity, ownership, classification, and policy constraints.

• Familiarity with BI tools and an understanding of how semantic models facilitate governed self-service analytics.

• Proven track record of reviewing models from other teams for accuracy, quality, and alignment with shared definitions.

🏝️ Benefits

• Competitive pay within the listed range + Bonus + Benefits + Equity

• Temporary benefits package (applicable after 60 days of employment)