Senior Analytics Engineer

• 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.

• 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.

• Strong stakeholder-management skills; comfortable leading conversations with business partners to gather requirements, manage expectations, and present findings.

Qualifications & Experience

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, Statistics/Mathematics, or a related field (or equivalent practical experience).

• 6+ years of hands-on experience in analytics engineering, data engineering, or related roles, with demonstrated depth in Databricks, data modeling, and AWS.

• Required: significant production experience building and optimizing pipelines and data models on Databricks and Delta Lake at scale.

• Required: strong, demonstrable data modeling experience (dimensional modeling, schema design) for analytics-ready datasets.

• Required: working experience with AWS services (S3, IAM, Glue, Lambda, or similar) in a production data platform.

• Preferred: experience with dbt on Databricks and Unity Catalog for governance and access control.

• Required: demonstrated experience mentoring junior developers and working alongside them in a hands-on capacity (pairing, reviews, shared delivery).

• Preferred: experience presenting to or working directly with business stakeholders (e.g., requirements gathering, readouts, roadmap discussions).

• Preferred: prior experience leading engineers.

• Added advantage: understanding of the pharma/biopharma domain and commercial datasets (e.g., claims, sales, payer, patient, HUB/specialty pharmacy), including common identifiers and integration challenges

We hire for skills and capabilities, not just credentials - if this role excites you, but doesn't perfectly match your resume, we encourage you to apply anyway.

How We Work

Where you work matters - because collaboration, innovation and patient impact happen in many settings. Our roles are structured across four work models: site-essential, site-by-design, field-based and remote-by-design. The model assigned to this role is based on its core responsibilities. Learn more at https://careers.bms.com/ways-of-working.