Senior Data Engineer: Build Scalable Analytics Pipelines

CVS Health Corporation; California (MO); Hybrid; USD 83,000 - 222,000; Full time

Benefits offered by this job

Medical coverage

Dental coverage

Vision coverage

Paid time off

Retirement plan

Wellness programs

Job summary

CVS Health Corporation is seeking a Sr. Data Engineer to design and implement data pipelines powering analytics.

You will be part of a team creating datasets for analytic workloads and play a key role in modeling the Core Data Domain to support various access patterns and use-cases. As a hands-on engineer, you will design data solutions, optimize pipelines, and collaborate with data scientists and analysts to enable BI and ML initiatives.

Qualifications

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

Responsibilities

• Model and optimize data models and schemas for analytics, reporting, and ML.
• Design and build ETL/ELT pipelines from multiple sources.
• Tune performance, partitioning, and clustering to reduce costs.
• Establish data quality, governance, and security policies.
• Collaborate with data scientists to integrate with BI and ML models.
• Create documentation and mentor junior data engineers.
• Design data infrastructure for analytical workloads and data marts.

Skills

SQL

Python

ETL pipelines

Cloud environments

Data governance

Communication

Education

Bachelor's degree

Job description

CVS Health Corporation is seeking a Sr. Data Engineer to design and implement data pipelines powering analytics.

You will be part of a team creating datasets for analytic workloads and play a key role in modeling the Core Data Domain to support various access patterns and use-cases. As a hands-on engineer, you will design data solutions, optimize pipelines, and collaborate with data scientists and analysts to enable BI and ML initiatives.