Manager, Analytics Engineering

CVS Health•Prior Lake, IL

23h•Remote

About The Position

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Position Summary We’re looking for a Manager of Analytics Engineering to lead a team that builds and manages finance data used by the Pharmacy Services CFO organization. In this role, you’ll design reliable, scalable data pipelines and models that bring together data from many sources. You’ll work closely with finance leaders and analytics partners to ensure high‑quality data is available to support timely, data‑driven decisions.

Requirements

5+ years of advanced SQL experience (e.g., BigQuery, SAP HANA, Teradata)
5+ years of experience using Python or another object‑oriented language in data or analytics engineering
5+ years working with data pipelines and orchestration tools (e.g., Airflow, Spark, Kubernetes)
3+ years of experience deploying and managing CI/CD in cloud data platforms
3+ years of experience working with serverless and cloud‑based architectures
Bachelor’s degree in a quantitative or STEM field, or equivalent experience.

Nice To Haves

Master’s degree in a STEM field
Experience building production‑ready, low‑maintenance data solutions
Experience with Kimball dimensional data modeling
Strong problem‑solving skills and ability to work through ambiguity
Experience working in agile environments
Strong communication skills and ability to influence across teams

Responsibilities

Lead, coach, and provide technical guidance to a team of analytics engineers
Design and oversee scalable data pipelines, models, and architecture
Partner with business and analytics stakeholders to turn complex questions into usable data solutions
Build and automate data extraction, transformation, and loading (ETL) processes using industry standards
Improve existing data pipelines by identifying performance, reliability, and quality issues
Develop tools and standards to improve code quality, consistency, and development speed
Create clear documentation to support long‑term sustainability
Collaborate closely with data visualization and reporting teams