Manager, Data and AI Analytics

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.

Position Summary:

The Manager, Data & AI Analytics Engineering is a hands-on individual contributor responsible for designing, building, and delivering scalable data and AI-driven analytics solutions supporting Global Patient Operations (GPO).

This role plays a critical part in advancing the GPO Analytics Hub, enabling standardized, high-quality data models, cross-platform data integration, and AI/automation use cases to drive operational insights and decision-making across patient operations, manufacturing logistics, scheduling, supply chain, and workforce analytics.

Key Responsibilities:

• Collaborate with cross-functional teams (SCLT, APH Ops, BI&T, Manufacturing, Supply Chain) to translate business needs into scalable data solutions

• Design, develop, and maintain scalable ETL pipelines and data models using SQL, Python, dbt, and Databricks (Medallion architecture – bronze/silver/gold)

• Build and optimize complex SQL transformation pipelines integrating data from SAP, Oracle, Salesforce, AWS Athena, PostgreSQL, and other enterprise systems

• Develop and maintain curated datasets and semantic models supporting the GPO Analytics Hub and enterprise reporting

• Design and optimize Power BI and Tableau datasets/dashboards to enable consistent KPI reporting and executive analytics

• Partner with analytics and business teams to standardize KPIs, definitions, and data logic across GPO

• Enable and support AI/automation initiatives, including predictive analytics, Copilot, and agent-based solutions

• Develop data harmonization and reconciliation pipelines to unify cross-system operational data

• Build reusable frameworks for ETL orchestration, API integration, automated validation, and operational alerting

• Implement data quality checks, monitoring, and production-ready deployment strategies

• Optimize performance of large-scale datasets across PostgreSQL, Impala/Cloudera, and Athena environments

• Support platform migration initiatives (e.g., Tableau to Power BI) and enterprise data standardization efforts

• Lead requirements gathering, UAT coordination, stakeholder reviews, and production rollouts

• Maintain documentation, metadata mapping, and data lineage to support governance and transparency

• Provide technical leadership, code reviews, and best practices guidance (no direct reports)

Key Qualifications & Experience:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Analytics, or related field

• 8+ years of experience in data engineering or analytics engineering

• Strong hands-on expertise in SQL and Python, with experience in large-scale data environments

• Experience with Databricks, dbt, and cloud data platforms (AWS/Azure)

• Proven ability to design and optimize ETL pipelines, data models, and semantic layers

• Experience with Power BI and/or Tableau

• Understanding of AI/ML data workflows and integration requirements

• Experience in life sciences, pharmaceutical, or regulated environments

• Hands-on experience with GenAI, LLMs, or agent-based AI solutions

• Experience with Data Ops, CI/CD, and Git-based development workflows

• Familiarity with Domino Data Lab or similar platforms

• Knowledge of data governance, metadata, and lineage frameworks

• Strong hands-on technical expertise with end-to-end ownership

• Ability to translate business requirements into scalable data solutions