Jobs at Bristol Myers Squibb

The Commercial Data & AI HYD team is seeking an Analytics Engineer I to help build and support the analytics layer on Databricks that connects curated data to the dashboards, self-service BI, and advanced analytics used across the business. This role is responsible for developing well-modeled datasets, metrics, and semantic assets that are easy for analysts and business users to consume.

This role operates as part of a deployed engineering team, working alongside an assigned Commercial business domain (e.g., Sales, Marketing, Market Access, or Medical Affairs) under the guidance of senior engineers to help keep the analytics layer aligned with that domain's needs.

The successful candidate will help analyze business and analytics requirements and build, test, and integrate the application components, tools, and utilities that support commercial reporting and analytics consumption, applying data modeling, SQL transformation, data quality, documentation, and governance practices under the guidance of senior engineers on the team.

This is an early-career role that offers strong exposure to Databricks-based analytics engineering, with the opportunity to build technical depth and grow into more senior data modeling and platform responsibilities over time.

• 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.

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.

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.