Director - Principal Engineer, Digital R&D DP&TS Platform and Data Engineering

Role Summary

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 accelerate the journey from target identification to clinical translation by leveraging advanced digital technologies, AI, and data-driven insights.

We are building a high-impact, outcome-oriented platform engineering team focused on enabling scalable, secure, and resilient infrastructure and developer experiences. We are seeking a dynamic and technically accomplished Principal Engineer who will lead the design and development of platform and data engineering solutions that empower scientists to generate, analyze, and interpret complex biological and preclinical data at scale. You will be part of a team of well-rounded generalists who thrive on solving complex problems, automating everything, and driving innovation across cloud-native environments. This role is critical to building cutting-edge platform capabilities that power scalable secure, and innovative advanced analytics, machine learning, and data science across the R&D organization.

This role is not narrowly defined. We are seeking self-starters who take initiative, adapt quickly, and possess a diverse skill set. You will be expected to learn rapidly, collaborate effectively, and deliver impactful outcomes.

Responsibilities

• Architect Scalable, Secure Data Platforms for Scientific Discovery: design and develop infrastructure that enables seamless ingestion, processing, and analysis of high-dimensional biomedical, omics, and clinical datasets; ensuring reproducibility, compliance, and performance.
• Champion DevSecOps Across Research and Analytical Workflows: embed security and automation into development pipelines, ML model development, and platform operations to ensure privacy, compliance, security and traceability.
• Foster Collaboration Across Scientific and Engineering Teams: bridge domain and technical expertise to align platform capabilities with research goals. Cultivate shared understanding and agility across integrative biology, computational research, and engineering teams.
• Influence Technical Direction and Cross-Functional Alignment: shape engineering roadmaps and advocate for cohesive platform strategies that balance innovation, risk management, and business priorities.
• Govern Platform Standards and Lifecycle Accountability: establish and uphold platform design standards, lifecycle policies, and governance models that maintain flexibility and scalability without compromising control or integrity.
• Uphold System Reliability and Analytical Accuracy: oversee platform health through robust observability, automated testing frameworks, incident response strategies, lifecycle governance, service level agreements, and audit trails that meet regulatory, compliance and industry principles (GxP, FAIR, etc.).
• Advance Responsible Innovation and Domain-Specific AI Adoption: identify emerging technologies and champion thoughtful experimentation with AI/ML techniques while ensuring transparency, interpret ability, and ethical data use in R&D contexts.
• Lead and Mentor Talent in Platform Engineering: guide engineers and scientists in best practices, critical thinking, and cross-disciplinary collaboration to build future-ready data and analytical platforms. Lead and grow a high-performing team of software and infrastructure engineers. Foster a culture of continuous learning, elevating technical excellence, and shaping leadership potential.
• Govern Platform Lifecycle and Scientific Data Stewardship: define standards and stewardship models for managing diverse research assets; balancing agility, traceability, and compliance throughout discovery lifecycles.

Qualifications

• Education: Bachelor‚Äôs degree in a relevant field (e.g., Computer Science, Data Science, Bioinformatics, Engineering, or related discipline)
• 8+ years of hands-on infrastructure and software engineering experience
• Architecting scalable cloud-based platforms (e.g., AWS, Azure)
• Building and securing data pipelines and infrastructure supporting advanced analytics and machine learning
• Leading DevSecOps initiatives in regulated environments (e.g., GxP, HIPAA)
• Proficiency in Python, Typescript, Java, or other modern high-level language
• Expertise in infrastructure-as-code tools (e.g., Terraform, Ansible, CloudFormation, Helm) and CI/CD (e.g., GitHub Actions)
• Deep understanding of modern data architectures (e.g., lakehouses, distributed systems)
• Demonstrated experience in working with regulated data, compliance frameworks, and secure development practices
• Ability to lead complex engineering efforts across global, cross-functional teams
• Fluent in English; capable of clear technical communication across scientific and engineering disciplines
• Demonstrated 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.

Preferred Qualifications

• Education: Master‚Äôs or PhD in a relevant field (e.g., Computer Science, Data Science, Bioinformatics, Engineering, or related discipline)
• Experience supporting pharmaceutical R&D, life sciences, or computational biology
• Familiarity with biomedical data standards (e.g., HL7 FHIR, CDISC) and FAIR data principles
• Proven success in designing AI/ML platforms for scientific discovery or clinical research
• Strong record of mentoring and growing engineering talent in high-complexity domains
• Thought leadership in platform strategy, ethical AI adoption, and responsible innovation
• Experience influencing cross-functional decisions, especially at the intersection of science and technology

Additional Requirements

• Travel up to 10% may be required for business activities.
• Work Location Assignment: On Premise