Principal Engineer, Data Engineering

Pfizer’s mission to deliver breakthroughs that change patients’ lives is rooted in our commitment to science and innovation. Within Discovery, Preclinical, and Translational Solutions (DP&TS), we work to shorten the path from target identification to clinical translation with better software, data, and AI.

We are looking for a Principal Engineer, Data Engineering to lead the architecture and delivery of the data engineering capabilities DP&TS depends on. The work covers lakehouse-style data platforms, cloud-native ingestion and transformation pipelines, data quality and observability, metadata and lineage, and curated, domain-oriented datasets that feed analytics, AI/ML, and scientific computing.

This is a hands-on technical leadership role for someone who can set direction and still go deep. You will move between architecture, code, and operations, and pick up unfamiliar problems quickly.

You will report to the Senior Director, Platform and Data Engineering, and work closely with the Group Product Manager, Data Engineering. You will set technical direction for the data engineering roadmap, work with scientific and product leaders to define data-as-a-product outcomes, and guide engineers in building data systems that are reliable, compliant, and cost-aware. You will lead an automation-first approach (CI/CD and infrastructure-as-code), consistent patterns, and the operational discipline that regulated environments and sensitive scientific data require.

The role is global. You will lead the data engineering team day to day, mentor engineers, set architecture and standards across teams, and be measured on real improvements in time-to-data, data trust, and platform reliability.

Key Responsibilities

• Design and evolve the lakehouse-style data architecture that supports batch and streaming ingestion, large-scale processing, and secure data access for analytics and AI/ML.
• Establish reusable patterns for ingestion, transformation, orchestration, and data modeling that speed up delivery and keep work consistent and maintainable.
• Define and run data quality, observability, and reliability practices for critical datasets: automated checks, monitoring and alerting, lineage-aware troubleshooting, SLOs and SLAs, and incident response.
• Work with domain teams to publish curated, discoverable datasets with clear ownership, documentation, metadata, and access controls, so data gets reused rather than rebuilt.
• Build security, privacy, and compliance into the pipelines from the start: encryption, secrets management, access control, auditing, and retention, in line with regulated environments and enterprise policy.
• Improve performance and cost across compute and storage through efficient processing, sensible storage layout and lifecycle, workload tuning, and clear cost visibility.
• Set the bar for engineering practice: CI/CD for data, infrastructure-as-code, repeatable environment promotion, and testing that makes delivery predictable and auditable.
• Lead and mentor the data engineering team through design reviews, architecture decisions, coaching, and cross-team alignment, building a culture of ownership and steady improvement.

BASIC QUALIFICATIONS  

Education: Bachelor’s degree in a relevant field (e.g., Computer Science, Data Engineering, Software Engineering, Data Science, Bioinformatics, or related discipline)

Experience:

• 8+ years of hands-on data/software engineering experience delivering production data platforms and pipelines in cloud environments.
• Architecting and building cloud-native data platforms (e.g., AWS, GCP, Azure)
• Designing scalable batch and streaming data pipelines, transformation frameworks, and curated datasets for analytics/AI
• Implementing operational excellence: monitoring, data quality automation, incident response, and reliability practices
• Working with regulated or sensitive datasets, applying secure development practices and auditability
• 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 outcome