Manager - Data and Analytics Engineering

Manager, Data and Analytics Engineer

ROLE SUMMARY

Use Your Power for Purpose 

At Pfizer, our purpose—Breakthroughs that change patients’ lives—drives every decision we make. Digital & Technology accelerates this mission by turning data into insights that power smarter science, stronger operations, and an exceptional colleague experience. Within this organization, the Enabling Functions Creation Center (EFCC) supports HR, Finance, Global Business Services, and Legal with the digital capabilities they need to operate effectively and unlock value.

As a hands‑on Manager - Data & Analytics Engineer, you will lead and build innovative data solutions that strengthen our enterprise data foundation, empower our enabling function partners, and help unleash the power of our people—ultimately supporting the breakthroughs that matter most to patients.

ROLE RESPONSIBILITIES

• As a hands-on engineer, you will build scalable data pipelines to provide accurate and impactful business analytics and insights

• Design and implementation of data architecture and infrastructure.

• Lead the development of data management strategies and policies.

• Manage a team of project data engineers and analysts, providing guidance and mentorship.    

• Ensure data quality and integrity across all data platforms. 

• Collaborate with cross-functional teams to align data initiatives with business goals. 

• Develop and maintain data governance frameworks. 

• Oversee the integration of new data technologies and tools. 

• Ensure compliance with data privacy regulations and standards. 

• Drive the optimization of data processing workflows and pipelines. 

• Lead the development of analytics solutions to support business decision-making. 

• Manage relationships with external data vendors and partners. 

• Oversee the creation and maintenance of data documentation and metadata. 

• Develop and monitor key performance indicators for data initiatives. 

• Ensure the scalability and performance of data systems. 

BASIC QUALIFICATION

• Candidates should possess a Bachelor's or MBA/MS/M.Tech with at least 5-10 years of relevant experience, a PhD with any years of relevant experience

• Data Architecture Design: Designing and structuring modern databases and modern data systems: Expert 

• Data Warehousing: Building and managing data warehouses (Preferably Snowflake): Expert 

• SQL: Advanced querying and database management: Expert 

• Data pipelines / ETL Processes: Designing and managing modern ETL (Extract, Transform, Load) processes and data engineering pipelines: Expert 

• Data Integration: Combining and transforming data from different sources: Expert

• Cloud Platforms (e.g., AWS, Azure, Google Cloud): Managing data infrastructure on cloud platforms: Advanced 

• Big Data Technologies (e.g., SnowFlake, Data Bricks, Spark): Handling and processing large datasets: Advanced 

• Data Modeling: Creating data models to support analytics: Advanced 

• Visual Analytics and Business Intelligence Tools: Using BI tools to derive insights from data: Advanced

• Product Roadmap: Own and manage data and analytics product roadmap and lifecycle

• Data Governance: Implementing policies and procedures for data management: Advanced 

• Data Visualization Tools (e.g., Tableau, Power BI): Creating visual representations of data and data story telling: Advanced

• Hands on experience with vibe coding and Generative AI based data pipeline and analytics solutions development to increase efficiency, reduce overall delivery cost and reduce time to market.

• Programming Languages (e.g., Python, R): Writing code for data manipulation and analysis: Expert

• Data Security: Implementing security measures to protect data: Intermediate 

• Data Quality Management: Ensuring accuracy and consistency of data: Advanced 

• Statistical Analysis: Applying statistical methods to analyze data: Intermediate 

• Leadership: Guiding and motivating a team to achieve goals: Expert 

• Strategic Thinking: Planning and executing long-term data strategies: Expert 

• Communication: Clearly conveying complex data concepts to stakeholders: Advanced 

• Problem Solving: Identifying and resolving data-related issues: Advanced 

• Collaboration: Working effectively with cross-functional teams: Advanced 

PREFERRED QUALIFICATIONS

• People Analytics experience using SaaS tools such as Visier, One Model, Perceptyx, Workday Prism Analytics, Workday People Analytics, SAP Success Factors Workforce Analytics is a big plus. Familiarity with cloud/SaaS-based Human Capital Management (HCM) systems such as Workday is a big plus.

• Experience with Global HR data integration and prior experience with Mergers, Acquisitions, and Divestitures is a plus.

• Familiarity with SoX, EU Global Data Privacy Regulations (GDPR) and other related international regulations is nice to have. Prior experience with data architecture designs and data engineering development related to the GDPR and data privacy guiding principles such as data minimization, right to be forgotten, etc is nice to have.

• Experience with Software engineering best practices, including but not limited to version control (Git/GitHub, TFS, Subversion, etc.), CI/CD (Jenkins, Maven, Gradle, etc.), automated unit testing, Dev Ops is highly beneficial but not required.

• Experience with sourcing and modeling data from application APIs and publishing data and analytics services via APIs / Data Services is highly beneficial but not required · Experience deploying through an agile methodology and working in a SCRUM or SAFe team is highly beneficial but not required.

• 6 or more years of experience with one or more general-purpose data processing programming languages, including but not limited to: SQL, Scala, Python, Java, etc

• Architected end-to-end data pipelines with a major cloud stack is a plus · Experience in Cloud computing, machine learning, text analysis, NLP, and developing and deploying data and analytics services such as recommendation engines experience is a plus

• Domain experience in the Human Resources field

Emerging skills:

• Machine Learning: Applying machine learning techniques for data analysis: Intermediate 

• Adaptability: Adjusting to new technologies and methodologies: Intermediate 

• Critical Thinking: Analyzing data critically to derive insights: Advanced 

• Time Management: Prioritizing tasks to meet deadlines: Advanced

• Decision Making: Making informed decisions based on data insights: Advanced 

 
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.

Information & Business Tech

Experience Level

Mid Level