Analytics Engineer I , Commercialization

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.

Key Responsibilities

• Design, build, and support analytics-ready data products, curated datasets, and reusable transformation assets within a modern lakehouse environment to enable commercial reporting, self-service analytics, and business insights.

• Translate business and analytics requirements into clear data specifications, dimensional models, metrics definitions, and dataset readiness criteria aligned to internal modeling and delivery standards.

• Develop and maintain scalable SQL- and Python-based transformation logic that integrates structured and semi-structured pharma datasets such as claims, patient, sales, payer, HUB, and specialty pharmacy data.

• Apply internally developed accelerators, reusable code patterns, templates, and engineering guardrails to improve development speed, consistency, and maintainability across data products.

• Create analytics-friendly models, including fact and dimension tables, cross-domain joins, slowly changing dimensions, and business-rule-driven metrics that support dashboard, reporting, and advanced analytics consumption.

• Implement standardized data quality checks, reconciliation logic, validation routines, and anomaly detection controls to ensure datasets are accurate, complete, and trusted by downstream users.

• Document data products, metric definitions, lineage, assumptions, and known limitations in accordance with internal documentation and governance standards.

• Work as part of a deployed engineering team embedded alongside an assigned Commercial business domain, partnering with data engineers, analysts, BI developers, and business stakeholders to support dashboard and reporting use cases.

• Apply data governance standards, access controls, and compliant handling practices for sensitive and regulated data, including PII/PHI awareness.

Skills & Competencies

• Strong proficiency in SQL for analytics transformations, data modeling, validation, and performance tuning.

• Working knowledge of Python for data preparation, validation, automation, and analytical workflows.

• Experience developing analytics-ready datasets using dimensional modeling concepts such as facts, dimensions, grains, keys, and slowly changing dimensions.

• Familiarity with ETL/ELT patterns, lakehouse concepts, and medallion architecture, including bronze, silver, and gold/refined layers.

• Hands-on familiarity with Databricks notebooks, jobs/workflows, and Delta Lake concepts.

• Understanding of semantic layer concepts, metric definitions, and how curated datasets support BI, dashboards, and self-service analytics.

• Experience with data quality practices including profiling, cleansing, standardization, reconciliation, and anomaly detection.

• Working knowledge of governance and secure data handling practices, including documentation, lineage, and access controls.

• Familiarity with BI/visualization tools such as Tableau or Power BI.

• Understanding of engineering practices such as Git/version control, code reviews, and basic CI/CD concepts.

• Strong problem-solving and communication skills, with the ability to work effectively with both technical and business stakeholders.

• Willingness to work as part of a deployed team embedded within a business domain, learning to adapt to domain-specific priorities.

Qualifications & Experience

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

• 1–3 years of hands-on experience in analytics engineering, data engineering, business intelligence engineering, or a related role building curated datasets and analytics-ready assets.

• Experience transforming large-scale structured and semi-structured datasets using SQL and Python, with attention to data quality and performance.

• Preferred: experience with Databricks, Delta Lake, or lakehouse architecture.

• Preferred: experience working with commercial pharma datasets such as claims, sales, payer, patient, HUB, or specialty pharmacy data.

• Preferred: exposure to BI/reporting use cases, semantic layer design, or dashboard-ready data modeling.

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.

Supporting People with Disabilities

BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles. Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer. If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to adastaffingsupport@bms.com . Visit careers.bms.com/eeo-accessibility to access our complete Equal Employment Opportunity statement.

Candidate Rights

BMS will consider qualified applicants with arrest and conviction rec