Senior Data Engineer: Build Scalable Data Pipelines & Analytics

8350 Oak Street Health MSO LLC; Illinois; On-site; USD 83,000 - 222,000; Full time

Benefits offered by this job

Comprehensive benefits

Job summary

CVS Health seeks a Sr. Data Engineer to design and implement data pipelines powering analytical capabilities. This hands-on role requires deep SQL, Python and cloud experience to translate business needs into scalable data solutions.

You will model Core Data Domain, build ETL/ELT pipelines, ensure data quality and governance, and collaborate with data scientists and analysts to enable BI and ML workloads across the enterprise.

Qualifications

• Strong proficiency in SQL and complex queries.
• Experience in Python for ETL pipelines.
• Experience deploying data pipelines in cloud environments (Azure, AWS, GCP).
• Knowledge of data governance best practices in cloud environments.
• Excellent communication and collaboration with data scientists, analysts, and product owners.

Responsibilities

• Data Modeling & Architecture: Develop data models and schemas for analytics and ML.
• Data Pipeline Development: Design and build ETL/ELT pipelines ingesting data from multiple sources.
• Performance Optimization: Tune queries, partitioning, and clustering to reduce costs.
• Data Quality & Governance: Enforce data quality standards and security policies.
• Data Integration & Transformation: Collaborate with data scientists to support BI and ML workloads.
• Documentation & Knowledge Sharing: Create docs and mentor junior engineers.
• Design and architect data infrastructure for analytical workloads.
• Understand data warehousing concepts and data marts.

Skills

SQL

Python

ETL/ELT

Cloud

Data governance

Communication

Education

Bachelor's degree

Job description

CVS Health seeks a Sr. Data Engineer to design and implement data pipelines powering analytical capabilities. This hands-on role requires deep SQL, Python and cloud experience to translate business needs into scalable data solutions.

You will model Core Data Domain, build ETL/ELT pipelines, ensure data quality and governance, and collaborate with data scientists and analysts to enable BI and ML workloads across the enterprise.