BASIC QUALIFICATIONS

• Bachelor’s degree in computer science, Information Technology, or similar field
• Experience working with on‑premises and cloud‑based data platforms (e.g., AWS, Azure, or hybrid environments).
• Experience working with modern cloud data platforms and warehouses, such as Snowflake, Microsoft Fabric, or equivalent technologies.
• Hands‑on experience with relational database technologies, including Aurora, PostgreSQL, Oracle, or similar enterprise databases.
• Familiarity with data quality, monitoring, and operational best practices for production workloads.
• Experience with ETL Tools (AWS Glue, Informatica, Talend, etc.)
• Experience implementing CI/CD pipelines for data workloads using tools such as Git, Azure DevOps, or GitHub Actions.
• Exposure to AI‑enabled or automation techniques applied to data engineering (e.g., accelerating analysis, improving data quality, or supporting scalable processing).
• Ability to produce clear technical documentation and collaborate effectively with cross‑functional stakeholders
• Excellent written and verbal communication skills
• Excellent organizational and time management capabilities
• Strong interpersonal skills in building customer relationships
• Ability to work independently and in a global team setting across time zones.

PREFERRED QUALIFICATIONS

• Exposure to DevOps practices, including automation, deployment, and operational support.
• Working knowledge of Power BI, including supporting analytics and reporting use cases.
• Cloud certification or equivalent hands‑on experience with AWS and/or Azure.
• Experience or familiarity with React for building or integrating web‑based applications.
• Pharmaceutical life science domain experience

ROLE RESPONSIBILITIES

• Design and Implement data models to support large scale business intelligence reporting
• Drive harmonization of data definitions, transformations, and reusable components across platforms.
• Identify gaps and improvement opportunities (quality, performance, reliability) and propose remediation actions.
• Design and build end-to-end data pipelines and curated datasets across on‑prem and cloud environments.
• Operate and support production workloads with monitoring, logging, and operational best practices.
• Apply AI where it adds value (e.g., accelerating analysis, improving data quality, enabling scalable processing).
• Maintain clear technical documentation (data flows, specs, operational notes) and collaborate with stakeholders to align with standards
• Contribute to CI/CD pipelines and automated strategies.
• Collaborate and partner across high-performing, cross-functional software engineering teams
• Partner with stakeholders to translate business needs into technical solutions
• Model ownership behaviors and accountability for team outcomes.

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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