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

Pfizer
Banstead
3 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst - Associate Director - Belfast

2 x Data Analyst - Local Authority

Performance and Data Analyst (SEND)

2 x Data Analyst - Local Authority

Digital Data Consultant, Data Engineering, Data Bricks, Part Remote

Commercial Data Analyst

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.


ROLE RESPONSIBILITIES

Reporting to the Senior Director of DP&TS Platform and Data Engineering, this role will define and deliver the platform design and architecture strategy that supports solution delivery, ensuring alignment with scientific goals, regulatory requirements, and enterprise digital strategy. You will lead a team of platform engineers and collaborate closely with product, data science, and Digital partners to deliver secure, scalable, and high-performance capabilities. You will have direct reports with direct accountability for setting direction, deploying resources, and leading pay, performance, goal setting, and development discussions.


In this role, you will drive architectural decision, champion engineering excellence, and steer cross-functional collaboration to deliver resilient, high-impact solutions. This global role empowers you to shape infrastructure strategies, mentor technical talent, and influence product direction across regions and business units. You will also be a catalyst for DevSecOps adoption, embedding security and automation seamlessly into the development lifecycle to ensure trusted delivery at scale.


Key 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, interpretability, 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.


BASIC QUALIFICATIONS

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


8+ years of experience 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


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.


PREFERRED QUALIFICATIONS

Education: Master’s 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


NON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS

Travel up to 10% may be required for business activities.


Work Location Assignment : On Premise

The annual base salary for this position ranges from $156,600.00 to $261,000.00.



  • In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 20.0% of the base salary and eligibility to participate in our share based long term incentive program. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life’s moments. Benefits offered include a 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution, paid vacation, holiday and personal days, paid caregiver / parental and medical leave, and health benefits to include medical, prescription drug, dental and vision coverage. Learn more at Pfizer Candidate Site – U.S. Benefits | (uscandidates.mypfizerbenefits.com). Pfizer compensation structures and benefit packages are aligned based on the location of hire. The United States salary range provided does not apply to Tampa, FL or any location outside of the United States.
  • The annual base salary for this position in Tampa, FL ranges from $141,000.00 to $235,000.00.

Relocation assistance may be available based on business needs and / or eligibility.


Sunshine Act

Pfizer reports payments and other transfers of value to health care providers as required by federal and state transparency laws and implementing regulations. These laws and regulations require Pfizer to provide government agencies with information such as a health care provider’s name, address and the type of payments or other value received, generally for public disclosure. Subject to further legal review and statutory or regulatory clarification, which Pfizer intends to pursue, reimbursement of recruiting expenses for licensed physicians may constitute a reportable transfer of value under the federal transparency law commonly known as the Sunshine Act. Therefore, if you are a licensed physician who incurs recruiting expenses as a result of interviewing with Pfizer that we pay or reimburse, your name, address and the amount of payments made currently will be reported to the government. If you have questions regarding this matter, please do not hesitate to contact your Talent Acquisition representative.


EEO & Employment Eligibility

Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E-Verify employer. This position requires permanent work authorization in the United States.


Pfizer endeavors to makewww.pfizer.com / careersaccessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process and / or interviewing, please email. This is to be used solely for accommodation requests with respect to the accessibility of our website, online application process and / or interviewing. Requests for any other reason will not be returned.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.