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 a Manager, Data Engineer, you will play a pivotal, hands-on role creating the data solutions that fuel our most advanced AI and analytics applications. By collaborating closely with subject matter experts and data scientists, you will develop the robust data models, pipelines, and feature stores required to 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 analysis, DT, Field Force sizing, On demand analytics, Data Strategy, Multi-Agents.
You will be central to delivering new, innovative capabilities by enabling the deployment of cutting-edge AI 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, translating roadmaps into 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
: Delivers the high-quality, performant data that forms the basis of meaningful recommendations and drives concrete strategic decisions for brand and commercial strategy.
• Innovation
: Stays abreast of analytical trends and cutting-edge applications 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 AI, maintaining high standards of quality and compliance in all developed solutions.
QUALIFICATION & EXPERIENCE
•
Bachelor’s, Master’s, or PhD in Computer Science, Statistics, Data Science, Engineering, or a related quantitative field.
• 6-9 years of experience in data or analytics engineering.
• Strong Python Skills: Proven ability to write clean, performant, and maintainable Python for data engineering, with proficiency in libraries like Polars, Pandas and Numpy.
• Modern Data Stack & Systems: Extensive hands-on experience with large-scale distributed systems, including dbt, 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 and a track record of 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, or Streamlit.
• 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: Demonstrates the ability to adjust and thrive in changing environments, embracing new tasks and applying knowledge in diverse contexts.
• Strong Communication: Exhibits effective communication skills for workplace interactions, including conveying ideas clearly and listening actively.
• Positivity: Maintains a 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.
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