Manager, Data and AI Analytics

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).

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
• 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
• Proven ability to design and optimize ETL pipelines, data models, and semantic layers
• Experience with Databricks, dbt, and cloud data platforms (AWS/Azure)
• 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
• Collaborative and proactive approach across cross-functional teams
• Strong problem-solving and system design skills
• Focus on data quality, consistency, and usability