Manager, Data Engineering

CVS Health

14d•$83,430 - $222,480•Onsite

About The Position

Oversight and responsibility for designing, building, and maintaining scalable data pipelines and architectures that enable reliable data access, analytics, and reporting. This role partners closely with analytics, data science, product, and business teams to ensure high-quality, well-structured data is available for decision-making and operational needs.

Requirements

3+ years of experience as a Data Engineer or in a similar data-focused role or equivalent experience
Strong experience with SQL and relational databases
Experience building ETL/ELT pipelines and working with large datasets
Familiarity with data warehousing concepts and dimensional modeling
Proficiency in at least one programming language (Python, Java, or Scala)
Experience with cloud platforms (AWS, Azure, or GCP)
Adept at execution and delivery (planning, delivering, and supporting) skills
Adept at business intelligence
Adept at problem solving and decision-making skills
Adept at collaboration and teamwork
Adept at growth mindset (agility and developing yourself and others) skills

Nice To Haves

Experience working within Healthcare Quality Management
Experience with NCQA HEDIS and other quality metrics
Hands-on experience with BI tools (Power BI, Tableau, Looker)
Exposure to data governance, metadata management, and security best practices

Responsibilities

Design, build, and maintain scalable data pipelines and ETL/ELT processes
Develop and optimize data models, data warehouses, and data lakes by working with stakeholders to understand their requirements and translate them into scalable and efficient data engineering solutions
Build, optimize, and deploy forecasting models using time-series modeling, machine learning, and MLOps practices
Apply advanced modeling and statistical analysis to Healthcare Quality metrics spanning NCQA HEDIS measures and CMS Clinical Stars
Apply advanced statistical, machine learning, and deep learning methods (e.g., ARIMA, Prophet, gradient boosting, etc.)
Performs code reviews and enforces coding standards, based on company and industry requirements, across the development teams
Ensure data reliability, accuracy, performance, and availability across systems
Integrate data from multiple sources including internal systems, APIs, and third-party vendors
Implement data quality checks, monitoring, and error-handling processes
Collaborate with analytics, BI, and data science teams to support reporting and advanced analytics
Optimize query performance and storage efficiency
Document data pipelines, schemas, and engineering standards
Support data governance, security, and compliance requirements
Troubleshoot and resolve data pipeline and system issues