Senior Data Engineer: Scalable Pipelines & Analytics

CVS Health; Clayton (NM); In loco; USD 83.000 - 222.000; Tempo pieno

Vantaggi offerti da questo lavoro

Medical coverage

Dental coverage

Vision coverage

Paid time off

Retirement plans

Descrizione del lavoro

CVS Health in the United States is seeking a Sr. Data Engineer to design and implement data pipelines powering analytics and data science workloads. This hands-on role requires deep expertise in data modeling, ETL/ELT processes, and cloud-based environments.

You will collaborate with data scientists and analysts to build scalable data architectures, ensure data quality and governance, and optimize performance and costs across data warehouses and data marts.

Competenze

• 5+ years of applicable work experience.
• Strong proficiency in SQL with complex queries.
• Experience in Python, ETL pipelines.
• Cloud deployment in Azure, AWS, or GCP.
• Knowledge of data governance best practices in cloud environments.

Mansioni

• Data Modeling & Architecture: Develop data models and schemas for analytics, reporting, and ML requirements.
• Data Pipeline Development: Design and build ETL/ELT pipelines ingesting data from multiple sources.
• Performance Optimization: Tune and optimize queries, partitioning, and clustering to reduce costs.
• Data Quality & Governance: Enforce data quality standards and security policies for storage and access.
• Data Integration & Transformation: Collaborate with data scientists to design data solutions for BI and ML.

Conoscenze

SQL

Python

Cloud environments

Data governance

Communication

Formazione

Bachelor's degree

Descrizione del lavoro

CVS Health in the United States is seeking a Sr. Data Engineer to design and implement data pipelines powering analytics and data science workloads. This hands-on role requires deep expertise in data modeling, ETL/ELT processes, and cloud-based environments.

You will collaborate with data scientists and analysts to build scalable data architectures, ensure data quality and governance, and optimize performance and costs across data warehouses and data marts.