Senior Analytics Engineer

About this role

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Skills you'll use

What you'll do

• Design, build, and own scalable analytics-layer transformations (marts, semantic models, and metrics) on Databricks, using strong dimensional and analytical data modeling practices and modern transformation frameworks (e.g., dbt on Databricks).
• Architect analytics-ready data models (star/snowflake schemas, conformed dimensions, fact tables) that translate complex commercial business logic into consistent, reusable data structures.
• Own performance tuning and cost optimization of Databricks workloads, including cluster configuration, Delta Lake optimization (Z-ordering, partitioning, OPTIMIZE/VACUUM), Unity Catalog, and job/workflow orchestration.
• Design and manage AWS-based data architecture underpinning the Lakehouse (e.g., S3 storage layout, IAM roles/policies, Glue, Lambda, and networking/security fundamentals) in partnership with cloud/platform teams.
• Architect and optimize datasets for large-scale, structured and semi-structured commercial datasets (e.g., sales, claims, patient, or similar), including complex cross-domain joins and slowly changing dimensions (SCD).
• Establish and enforce testing, validation, documentation, and CI/CD practices for Databricks-based analytics code, ensuring data quality, lineage, and reliability at scale.
• Lead technical design reviews and set best practices for data modeling, Databricks architecture, and AWS resource usage; mentor and provide technical guidance to data and analytics engineers.
• Mentor and coach junior developers day-to-day — pairing on code, reviewing pull requests, and working alongside them on shared deliverables to build their skills in data modeling, Databricks, and AWS.
• Act as a primary point of contact for business stakeholders, clearly explaining technical trade-offs, data model decisions, and dataset limitations in business-friendly terms.
• Partner closely with analysts, data scientists, and business stakeholders to translate ambiguous business questions into well-modeled, analytics-ready data products, and define dataset readiness and adoption criteria.

Who can apply

• Expert, hands-on experience with Databricks (notebooks, jobs/workflows, clusters, Unity Catalog) and Delta Lake (ACID tables, incremental processing, upserts/merge, Z-ordering, partitioning, performance tuning) — this is a core requirement.
• Strong expertise in data modeling for analytics — dimensional/Kimball-style star and snowflake schemas, conformed dimensions, fact tables, SCD handling, and lakehouse concepts (medallion architecture; bronze/silver/gold layers) — this is a core requirement.
• Solid, hands-on experience with AWS as the underlying cloud platform (S3, IAM, Glue, Lambda, networking/security fundamentals) supporting a Databricks Lakehouse — this is a core requirement.
• Advanced proficiency in SQL and Python for data transformation, modeling, and validation at scale.
• Hands-on experience with analytics engineering frameworks (e.g., dbt) and building governed semantic/metrics layers on top of Databricks.
• Experience with workflow orchestration (e.g., Databricks Workflows, Airflow) and engineering best practices (Git/version control, code review, CI/CD).
• Strong grounding in data governance and secure data handling (documentation, lineage, access controls via Unity Catalog/IAM, PII/PHI awareness).
• Proficiency with BI/visualization tools (Tableau/Power BI) and enabling self-service analytics for business users.
• Demonstrated ability to mentor and coach junior developers, including hands-on pairing and code review, while working alongside them as part of the same delivery team.
• Excellent verbal and written communication skills, with the ability to explain technical concepts clearly and confidently to non-technical business stakeholders.

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