Senior Analytics Engineer

Working with Us Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible. Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us .

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.

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,