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Our client’s purpose, breakthroughs that change patients’ lives, is rooted in being a science driven and a patient focused company. Digital, AI, data, and analytics are central to driving innovation at Pfizer.
What You Will Achieve
Manager, Data Engineer will be on a team responsible for the execution of the technical strategies and development of the digital solutions to support their colleagues across Finance, People Experience, Legal, Compliance, and other Enabling Functions.
Manager, Data Engineer will be part of an agile team designing, developing, and maintaining their data infrastructure, ensuring data quality and modeling leveraging their tool stack including Snowflake, Spark, Python, Airflow, and dbt.
How You Will Achieve It
- Design, development, and maintenance of scalable data pipelines and ETL processes.
- Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.
- Implement data quality checks and monitoring to ensure data accuracy and reliability.
- Optimize data storage and retrieval processes to improve performance and efficiency.
- Work with cloud-based data platforms and tools to manage and process large datasets.
- Ensure data security and compliance with relevant regulations and standards.
- Troubleshoot and resolve data-related issues and provide support to end-users.
- Create and maintain technical documentation.
Qualifications
Must-Have
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- At least 5 years of experience as a Data Engineer or in a similar role.
- Strong knowledge of SQL and experience with relational and non-relational databases, preferably Snowflake or other (Redshift, PostgreSQL).
- Proficiency in programming languages such as Python or Java, R, Scala.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Experience with DevSecOps practices such as CI/CD and automated testing to ensure the reliability and scalability of data pipelines.
- Experience with applying DataOps principles to improve the efficiency and quality of data workflows, including version control, monitoring, and collaboration.
- Experience with data quality and data modeling.
- Familiarity with microservices architecture and container services.
- Familiarity with big data technologies such as Spark, Hadoop.
- Familiarity with data orchestrators such as Airflow.
- Strong communication and collaboration skills.
Nice-to-Have
- Working experience with different types of data formats.
- Working experience with open source data engineering tools.
- Experience with data visualization tools such as Tableau or Power BI.
- Knowledge of machine learning and data science concepts.
Work Location Assignment: Hybrid
