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.
Information & Business Tech