ROLE SUMMARY

Pfizer’s purpose is to deliver breakthroughs that change patients’ lives. Research and Development is at the heart of fulfilling Pfizer’s purpose as we work to translate advanced science and technologies into the therapies and vaccines that matter most. Whether you are in the discovery sciences, ensuring drug safety and efficacy or supporting clinical trials, you will apply cutting edge design and process development capabilities to accelerate and bring the best in class medicines to patients around the world.

The Data Operations Lead is a hands‑on data engineering leader responsible for operating, stabilizing, and continuously improving a large‑scale enterprise data platform that provides trusted data to more than 400 AI and analytical solutions across Pfizer Global Supply.

This role leads a technical data operations team while remaining deeply involved in complex investigations, code reviews, and engineering decisions. The primary objective is to ensure data reliability, responsiveness, and trust at enterprise scale, by applying strong data engineering practices, enforcing coding and operational standards, and delivering predictable service outcomes for business‑critical analytics and AI workloads.

ROLE RESPONSIBILITIES

• Data Engineering Operations Leadership

  • Manage a hands‑on data engineering operations team responsible for supporting production data pipelines, databases, and AI data products. Ensure issues are investigated and resolved using strong engineering discipline, clear ownership, and consistent technical standards

• Hands‑On Data Engineering & Troubleshooting

  • Remain actively hands‑on in complex investigations involving Python code, SQL logic, data pipelines, transformations, and database behavior. Review code, debug data issues, validate fixes, and guide engineers toward durable solutions.

• Engineering Root Cause Analysis & Prevention

  • Drive deep technical root cause analysis across ingestion, transformation, and consumption layers. Ensure recurring issues are addressed through code improvements, refactoring, better validations, or architectural fixes, rather than temporary workarounds.

• Engineering Standards, Code Quality & Reviews

  • Define, enforce, and evolve data engineering coding standards, including Python and SQL best practices, version control discipline, and code review expectations. Ensure all operational fixes meet quality, reliability, and maintainability standards even under production pressure.

• SLA Ownership Through Engineering Excellence

  • Define, implement, and improve SLAs for data operations by reducing manual intervention, improving automation, and raising engineering quality. Track operational performance and continuously improve response and resolution outcomes through engineering improvements.

• AI Application Front‑Line Support

  • Serve as the front‑line technical leader for AI and data‑driven applications, supporting model outputs, data pipelines feeding AI solutions, feature/embedding generation, and downstream data consumers. Diagnose data‑related AI issues and ensure fixes align with engineering best practices.

• Database & Platform Reliability

  • Own operational reliability across data platforms and databases, including schema management, query performance, access patterns, and data correctness. Ensure production data behavior is well understood, monitored, and documented.

• Stakeholder Communication & Trust Restoration

  • Provide clear, technically grounded communication to stakeholders regarding data issues, impacts, and remediation actions. Set realistic expectations and rebuild trust through predictable execution, transparency, and engineering credibility.

Professional Experience and Educational Requirement

Education / Experience

• Bachelor’s degree (Master’s preferred) in Computer Science, Data Engineering, or a related technical field.

• 5 - 10 years of hands‑on Data Engineering experience, including operating and supporting production data systems.

• Experience leading or acting as a Technical Lead for Data engineering or Data operations teams.

Technical (Must‑Have)

• Strong hands‑on programming experience with one or more general‑purpose languages, including Python, SQL, Java, Scala, PySpark, C, C++, C#, Swift/Objective‑C, or JavaScript.

• Proven experience with data preparation, ingestion, and ETL/ELT frameworks, such as Airflow, dbt, Fivetran, Kafka, Informatica, Talend, Alteryx, or equivalent technologies.

• Strong experience with software engineering best practices, including version control (Git, TFS, Subversion), CI/CD pipelines (Jenkins, Maven, Gradle, or similar), automated unit testing, and DevOps practices.

• Hands‑on experience with cloud data platforms and storage technologies, such as Snowflake, Databricks, Amazon S3, Redshift, BigQuery, or equivalent platforms.

• Demonstrated experience architecting and operating end‑to‑end data pipelines, using cloud‑based and/or on‑premises stacks.

• Prior hands‑on experience as a data modeler is required, including dimensional modeling and analytical data model design.

• Strong understanding of database management fundamentals, including schemas, tables, views, permissions, query performance, and operational troubleshooting.

• Proven ability to diagnose and resolve data quality issues at the engineering level, including logic errors, transformation issues, and source‑to‑target alignment.

Leadership & Ways of Working

• Proven ability to lead a technical team while remaining hands‑on.

• Strong problem‑solving skills with a bias toward engineering-driven fixes.

• Ability to define and enforce SLAs in a technical operations environment.

• Strong stakeholder communication skills, especially in high‑impact data incidents.

PREFERRED QUALIFICATIONS

• Experience supporting AI or analytics applications in production environments.

• Experience operating data platforms in large‑scale or regulated enterprise environments.

• Familiarity with ITIL‑aligned incident/problem management applied pragmatically within engineering teams.

• Knowledge of cloud computing, machine learning, text analytics, NLP, and web‑based application architectures.

• Knowledge of ontologies and graph databases (e.g., Neo4j, Titan) and associated query languages is a plus.

• and information from varied data sources, both new and pre-existing, into discernable insights and perspectives; takes a problem-solving approach by connecting analytical thinking with an understanding of business drivers

• Adaptable: Demonstrates flexibility in the face of shifting targets, thrives in new situations

• Pioneering: Pushes self and others to think about new innovation and digital frontiers and ways to conquer them

• Ambiguity Tolerant: Successfully navigates ambiguity to keep the organization on target and deliver against established timelines

• Exceptional Communicator: Can understand, translate, and distill the complex, technical findings of the team into commentary that facilitates effective decision making by senior leaders; can readily align interpersonal style with the individual needs of customers

• Highly Collaborative: Manages projects with and through others; shares responsibility and credit; develops self and others through teamwork; comfortable providing guidance and sharing expertise with others to help them develop their skills and perform at their best; helps others take appropriate risks; communicates frequently with team members earning respect and trust of the team  

• Proactive Self-Starter: Takes an active role in one’s own professional development; stays abreast of analytical trends, and cutting-edge applications of data

• Creative: Able to bring forth new ideas to improve our existing practices and takes calculated risks to innovate new capabilities within Business Analytics, with a focus on data products and analytics solutions

 

NON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS

20% travel may be required based on delivery and project priorities

 
Work Location Assignment: Hybrid

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.

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