Senior Analytics Engineer

Bristol-Myers Squibb; United States; On-site; USD 130,000 - 170,000; Full time

Job summary

Bristol Myers Squibb is seeking an experienced Senior Analytics Engineer to design, build, and scale the analytics layer between raw data and business insights for commercial analytics. You will own the transformation logic, dimensional models, and metrics that power dashboards and self-serve BI on the Databricks Lakehouse.

You will mentor junior developers, collaborate with analysts and data scientists, and partner with business stakeholders to ensure data quality, governance, and scalable

Qualifications

• Expert hands-on experience with Databricks notebooks, jobs/workflows, clusters, Unity Catalog and Delta Lake
• Strong data modeling for analytics using star/snowflake schemas and lakehouse concepts
• Solid experience with AWS services (S3, IAM, Glue, Lambda) supporting a Databricks Lakehouse
• Advanced SQL and Python for data transformation, modeling, and validation at scale
• Experience with analytics engineering frameworks and governance of semantic/metrics layers
• Experience with workflow orchestration tools (Airflow, Databricks Workflows) and CI/CD practices
• Familiarity with data governance concepts, lineage, and security controls

Responsibilities

• Design, build, and own scalable analytics-layer transformations on Databricks
• Architect analytics-ready data models (star/snowflake) for business logic
• Own performance tuning and cost optimization of Databricks workloads
• Design and manage AWS-based data architecture underpinning the Lakehouse
• Architect and optimize large-scale datasets
• Establish testing, validation, and CI/CD practices for analytics code
• Lead technical design reviews and mentor engineers
• Mentor junior developers and collaborate with stakeholders to define metrics

Skills

Databricks expertise

Delta Lake

Data modeling analytics

AWS cloud platform

SQL and Python

dbt

Workflow orchestration

Git/CI-CD

Tools

Airflow

dbt on Databricks

Job description

At Bristol Myers Squibb, our employees often ask, "Who are you working for?"-a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it.

Position Summary

We are looking for an experienced Senior Analytics Engineer with deep expertise in Databricks, data modeling, and AWS to design, build, and scale the analytics layer that sits between raw curated data and business-facing insights for commercial analytics and reporting. In this role, you will own the transformation logic, dimensional/semantic models, and metrics definitions that turn refined-layer datasets into trusted, self-serve analytics products on the Databricks Lakehouse. You will set standards for data modeling and testing, mentor and work hands-on alongside junior developers to help them grow, and partner directly with analysts, data scientists, and business stakeholders to define metrics, data models, and governed datasets that power dashboards, self-service BI, and advanced analytics. You will also drive technical decisions on Databricks and AWS architecture, performance, scalability, and cost optimization across the analytics stack, while confidently communicating technical concepts and trade-offs to business audiences.

Key Responsibilities

• 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.
• Apply and champion data governance practices on Databricks and AWS, including documentation, lineage, access controls (Unity Catalog/IAM), and compliant handling of sensitive/regulated data.

Skills & Competencies

• 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 g