Data Engineer - People Analytics & Data Pipelines

ISHE; Boise (ID); On-site; USD 83,000 - 222,000; Full time

Benefits offered by this job

Medical, dental, and vision coverage

Paid time off

Retirement savings options

Wellness programs

Job summary

CVS Health is seeking a Data Engineer for the People Analytics data engineering team. The role focuses on building data pipelines, data warehouse modeling, and analytics workflows across HR and Workforce domains.

You will collaborate with HR leaders, develop robust data models, and enable reproducible analytics in Python, SQL, and cloud platforms while ensuring data governance and security.

Qualifications

• 3+ years' experience in Data Engineering.
• 3+ years' experience with ETL/ELT processes and tools.
• 3+ years' experience with SQL and relational databases.
• 2+ years' experience programming in Python for data manipulation, automation, and analysis.
• 3+ years' experience with cloud platforms (GCP preferred).
• 2+ years' experience with data warehousing (Snowflake/Redshift/BigQuery).
• 2+ years' experience with scheduling tools like Airflow or Composer.

Responsibilities

• Design, build, and manage robust data pipelines and ETL processes for HR data.
• Develop and optimize data workflows for accuracy, quality and availability.
• Oversee a team of data engineers and promote best practices.
• Collaborate with HR and stakeholders to structure data for analysis and reporting.
• Ensure data governance and security in accordance with policies and regulations.

Skills

Data engineering

Python programming

SQL

Data modeling

Education

Bachelor's degree in CS/Data Science/related

Master's degree preferred

Tools

GCP

Snowflake

BigQuery

Airflow

Redshift

Job description

CVS Health is seeking a Data Engineer for the People Analytics data engineering team. The role focuses on building data pipelines, data warehouse modeling, and analytics workflows across HR and Workforce domains.

You will collaborate with HR leaders, develop robust data models, and enable reproducible analytics in Python, SQL, and cloud platforms while ensuring data governance and security.