Senior Data Engineer: ETL & Analytics Pipelines

CVS Health; Yorktown (VA); On-site; USD 83,000 - 222,000; Full time

Job summary

CVS Health is seeking a Sr. Data Engineer to design and implement data pipelines powering analytical capabilities. This hands-on role requires strong data engineering practices and the ability to translate business requirements into technical solutions.

You will be part of a team creating datasets for analytic and data science workloads, modeling the Core Data Domain to support various access patterns and use cases, and collaborating with stakeholders to enable BI and ML initiatives.

Qualifications

• 5+ years of relevant work experience in data engineering.
• Strong proficiency in SQL and developing complex queries.
• Experience with Python for ETL pipelines.
• Experience deploying data pipelines on cloud platforms (Azure, AWS, GCP).
• Knowledge of data governance practices in cloud environments.
• Excellent communication and collaboration with data scientists, analysts, and product owners.

Responsibilities

• Model and architect data to support analytics and ML workloads.
• Design and build ETL/ELT pipelines ingesting data from multiple sources.
• Tune performance with partitioning and clustering to reduce costs.
• Establish data quality standards and data governance and security policies.
• Collaborate with data scientists to design integrated data solutions for BI and ML.
• Create thorough documentation and share best practices, mentoring juniors.
• Design data infrastructure for analytical workloads and data warehousing concepts.

Skills

SQL proficiency

Python ETL

Data modeling

Communication

Team collaboration

Education

Bachelor's degree

Tools

Azure

AWS

GCP

Job description

CVS Health is seeking a Sr. Data Engineer to design and implement data pipelines powering analytical capabilities. This hands-on role requires strong data engineering practices and the ability to translate business requirements into technical solutions.

You will be part of a team creating datasets for analytic and data science workloads, modeling the Core Data Domain to support various access patterns and use cases, and collaborating with stakeholders to enable BI and ML initiatives.