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 Bachelors 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 Information & Business Tech