Manager, Data Engineering

ROLE SUMMARY

The Global Commercial Analytics (GCA) team within the organization is dedicated to transforming data into actionable intelligence enabling the business to remain competitive and innovative in a data-driven world.

As aManagerData Engineer you will play a pivotal hands-onrolecreating the data solutions that fuel our most advanced AI and analytics applications. By collaborating closely with subject matter experts and data scientists you will developthe robustdata models pipelines and feature storesrequiredto power everything from statistical analysis to complex AI and machine learning models.

Your primary mission is to build the data foundation for high-impact projects such as ROI analysisDTField Force sizingOn demandanalytics Data Strategy Multi-Agents.

You will be central to delivering new innovative capabilities by enabling the deployment ofcutting-edgeAI and machine learning algorithms directly helping the business solve its most challenging problems and create value.

This is a dynamic fast-paced and highly collaborative role covering a broad range of strategic topics critical to the pharma business.

ROLES & RESPONSIBILITIES

Advanced Layer Development: Leads the development of core data components for our advanced analytics layer and agentic data layers enabling next-generation analytics and AI tools.

Data Strategy Execution: Works closely with cross-functional teams to help execute the enterprise Data Strategy translatingroadmapsinto technical designs and building solutions using standard technology stacks.

Building AI & RAG Pipelines: Designs and builds end-to-end data pipelines and products specifically to power advanced AI and Retrieval-Augmented Generation (RAG) applications for the Commercial Pharma domain.

Enabling Advanced Analytics: Builds the clean reliable data foundation that enables the use of statistical analysis machine learning and AI models like RAG to uncover patterns and insights.

Business Impact: Deliversthe high-qualityperformantdata that forms the basis of meaningful recommendations and drives concrete strategic decisions for brand and commercial strategy.

Innovation: Stays abreast of analytical trends andcutting-edgeapplications of data science and AI including RAG and agentic systems actively applying new techniques and tools to improve data pipelines.

Quality & Governance: Implements and adheres to best practices in data management model validation and ethical AImaintaininghigh standardsof quality and compliance in all developed solutions.

QUALIFICATION & EXPERIENCE

Bachelors Masters or PhD in Computer Science Statistics Data Science Engineering ora related quantitative field.

6-9years of experience in data or analytics engineering.

Strong Python Skills: Proven ability to write clean performant and maintainable Python for data engineering withproficiencyin libraries like PolarsPandasandNumpy.

Modern Data Stack & Systems: Extensive hands-on experience with large-scale distributed systems includingdbt Airflow Spark and Snowflake Snowflake Cortex Agents.

Data Modeling & Databases: A strong background in building and managing complex data models and warehouses with experience across both SQL and NoSQL databases.

Data Quality & Observability: Experience implementing and managing frameworks for data quality testing observability and alerting.

Modern Software Development: Solid experience with modern software development workflows including Git CI/CD and Docker to automate analytics processes.

Project Leadership: Experience mentoring other engineers and leading technical projects.

Good to Have:

Pharma Analytics:Experience with pharmaceutical data anda track recordof delivering business impact in the commercial pharma sector.

Data Engineering Best Practices:Experience with performance tuning cost optimization and managing large-scale data infrastructure.

Dashboard Development:Experience building dashboards using tools like Tableau Power BI orStreamlit.

Business Communication:The ability to explain data limitations and how they affect business questions to non-technical audiences.

Data Product Management:Familiarity with the principles of managing data as a product.

Professional Characteristics:

Adaptability and Flexibility:Demonstratesthe ability to adjust and thrive in changing environments embracing newtasksand applying knowledge in diverse contexts.

Strong Communication:Exhibitseffective communication skills for workplace interactions including conveying ideas clearly and listening actively.

Positivity:Maintainsa positive attitude showing a willingness to work hard and learn contributing to a harmonious work environment.

Self-Starter: Takes an active role in professional development; stays abreast of analytical trends and cutting-edge applications of data.

Work Location Assignment:Hybrid

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.