Director, Data Integration Engineer

Role Summary

This role owns data integration engineering for the Medical Affairs AI Acceleration portfolio. It ensures all solutions are integrated successfully to source data. Working as an individual-contributor technical expert within the Engineering organization, this role is directly accountable for the data pipelines and integration patterns that connect Medical Affairs AI solutions to existing enterprise data sources and analytic platforms. It ensures each new AI capability has reliable, well-governed access to the data it needs without duplicating or re-architecting the underlying data platforms.

• Design and build data pipelines and integration patterns connecting core enterprise systems to Medical Affairs' AI and analytics platforms and data sources.
• Build and maintain targeted data pipelines that extract, transform, and serve the specific data each AI solution needs, prioritizing reuse across solutions.
• Establish and follow data pipeline coding standards for solution-level integration work, aligning with the data models and cataloging practices maintained by the centralized data organization.
• Monitor and manage data pipeline latency, ensuring each AI solution receives data within the timeliness thresholds its use case requires.

• Partner with Solution Architecture to implement data integration patterns supporting RAG pipelines, vector databases, GraphRAG (graph-structured retrieval context for LLMs), and other AI/ML data access patterns.
• Ensure data feeding Agentic AI and LLM-based systems is well-governed, accurately labeled, and monitored for quality and drift.
• Apply automation techniques (AI/ML, low-code/no-code tooling where appropriate) to accelerate data delivery.
• Support context-aware and context-driven AI models by ensuring underlying data structures capture the necessary business context.

• Collaborate and partner with the centralized Commercial AI Data Strategy team to align on data profiling, sourcing and investigation.
• Apply data governance and data cataloging best practices, maintaining data dictionaries, lineage documentation, and playbooks.
• Ensure data integration practice complies with Pfizer data privacy and regulatory standards (GDPR, HIPAA, GxP as applicable).
• Own identification and classification of personal information (PI/PII) flowing through integration pipelines, and apply masking, tokenization, or de-identification before sensitive data is stored in a vector database or made accessible to AI/ML systems.

• Partner with the Senior Director, Engineering and Build Engineers to ensure data pipelines are delivered in step with product and platform build cycles.
• Partner with Solution Architecture to ensure data integration design aligns with enterprise architectural standards and reuse patterns.

• Drive best practices and world-class data engineering capability, staying current with modern data platform technology (e.g., Snowflake, graph databases) and AI-enabled data tooling.
• Establish a culture of high performance, transparency, and continuous improvement within the data integration discipline.

Candidate demonstrates a breadth of diverse leadership experiences and capabilities including: the ability to influence and collaborate with peers, develop and coach others, oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact.

• Bachelor's degree in Computer Science, Data Analytics or related field.
• 8+ years of hands‑on data engineering or application integration experience, including building data pipelines and APIs that connect…