Senior Data Engineer: ETL & Analytics Pipelines

CVS Health; Ohio; On-site; USD 83,000 - 222,000; Full time

Benefits offered by this job

Bonus eligibility

Comprehensive benefits package

Paid time off

Retirement savings options

Wellness programs

Job summary

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

You will be part of a dedicated team creating datasets for analytic and data science workloads. You will also have a key role in modeling the Core Data Domain to best support various access patterns and use‑cases.

Qualifications

• 5+ years of applicable work experience.
• Strong proficiency in SQL and ability to develop complex queries.
• Experience with Python for ETL pipelines.
• Experience deploying data pipelines in a cloud environment (Azure, AWS, GCP).
• Knowledge of data governance best practices in a cloud environment.

Responsibilities

• Data Modeling & Architecture: Develop and optimize data models and schemas for analytics, reporting, and ML.
• Data Pipeline Development: Design and build ETL/ELT pipelines from multiple sources.
• Performance Optimization: Tune performance, partitioning and clustering to improve queries and reduce costs.
• Data Quality & Governance: Establish data quality standards and security policies.
• Data Integration & Transformation: Collaborate with data scientists to design data solutions integrated with BI and ML models.
• Documentation & Knowledge Sharing: Create comprehensive docs and mentor junior engineers.
• Design and architect data infrastructure for analytical workloads and dimensional modeling.

Skills

SQL

Python

ETL pipelines

Cloud platforms

Communication

Education

Bachelor's degree

Job description

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

You will be part of a dedicated team creating datasets for analytic and data science workloads. You will also have a key role in modeling the Core Data Domain to best support various access patterns and use‑cases.