Pfizer’s purpose, breakthroughs that change patients’ lives, is rooted in being science driven and a patient focused company. Digital and Technology is the driving force of data and AI innovation at Pfizer.

The Data and AI Platforms team, which is part of the Artificial Intelligence Center of Excellence organization is responsible for the creating a seamless experience for engineers to harness data, analytics, machine learning, artificial intelligence, agentic, and visualization capabilities through a unified platform across the enterprise – from research and development, clinical, manufacturing, commercial and enabling functions across all geographies. The team is responsible for the self-service platform ecosystem, developer experience, and exploration of emerging technology.

Part of the Data and AI Platforms team are a group of data engineers who are responsible for enhancing the adoption of unified platform data frameworks, pipeline development practices, and platform across the business You will be responsible for designing, building, and maintaining the scalable data infrastructure, frameworks, and pipelines that power analytics and machine learning initiatives across the organization. This role emphasizes the application of DevOps and DataOps principles, ensuring an agile, automated, and collaborative approach to the entire data lifecycle.

In this role, you will act as the technical engineering lead for data solutions that function as standalone products or reusable platform templates for teams across the enterprise. You will need to balance leading cross-functional teams with hands-on development on the platform and managing stakeholder expectations. You will also be accountable for a team of around 5 colleagues.

You may have a software development background, or an engineering background or you were raised in a modern data analytics culture from the start of your career. You will have the ability to work with technology vendors, support resources, delivery partners, Pfizer stakeholders, and apply emerging and traditional technologies for analytics improvements in support of enterprise analytics platform strategy.

ROLE RESPONSIBILITIES

• Design, construct, install, test, and maintain robust and scalable Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) data pipelines to pull data from diverse internal and third-party sources.
• Build and manage enterprise data warehouses and data lakes, ensuring optimal performance and data integrity.
• Implement data platforms utilizing cloud services optimizing data delivery architecture to be highly available and fault-tolerant.
• Develop processes for data monitoring and implement data quality checks to ensure the accuracy, completeness, and reliability of core datasets.
• Optimize existing data systems for improved performance and efficiency, implementing automation where possible.
• Partner with data analysts, data scientists, and software engineers to understand and address data needs, ensuring the infrastructure supports analytical goals.
• Create and maintain documentation for all data pipelines, processes, and infrastructure, adhering to best practices in data engineering.
• Drive strategic reuse and user enablement by:
• Developing and maintaining reusable assets for consistency and scalability.
• Defining standards and best practices in partnership with platform owners.
• Assessing and adopting emerging enterprise platform capabilities.
• Delivering end-to-end data solutions to integrate advanced features into business workflows.
• Creating platform specific proof-of-concepts that demonstrate feasibility and readiness for enterprise utilization.
• Ensuring cross-functional alignment for cohesive data and AI adoption across the organization.