Executive Director, Data Engineering, Retail Analytics

CVS Health

Confirmed live yesterday High trust
Closes in 7 days Remote

Quick summary

Work type
Remote
Location
Woonsocket, RIConnecticutNew YorkTexasMassachusetts
Salary
$175,100–$334,750 / yr
Posted
24 days ago
Freshness
Confirmed live yesterday
Closes
Sep 18, 2026 (soon)

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $199k
This role $255k
$128k most similar roles pay here $357k

This role pays more than 80% of similar roles. Most pay $156,450–$242,212 — the shaded band above. At the midpoint, this role pays about $255k versus about $199k for comparable roles.

Based on 240 similar postings.

Employer

About CVS Health

CVS Health is a leading American healthcare company operating retail pharmacies, pharmacy benefit management services, and a health insurance segment through Aetna, one of the nation''s largest health insurers. Industry: Healthcare & Pharmacy

CVS Health currently has 88 open roles on FindRole.

Listed pay typically runs $118,450–$284,280 across 84 roles with salary data.

Most-posted roles

View all roles at CVS Health

At a glance

TL;DR · Executive Director, Data Engineering, Retail Analytics

The Executive Director, Data Engineering - Retail Analytics serves as a senior technology leader within the Analytics Engineering team, overseeing a large, multi-disciplinary organization. This individual is responsible for defining and executing the data engineering strategy to power the retail analytics ecosystem. The role involves designing, building, and scaling enterprise data platforms, AI products, and engineering capabilities that support pharmacy, supply chain, merchandising, personalization, pricing, marketing, and operational analytics. To achieve these goals, the leader must manage platform modernization, data governance, and distributed processing while collaborating with Data Science and Product teams to deliver cloud-based solutions. The position requires expertise in modern data engineering practices, including data pipelines and DevOps. The work focuses on solving complex business problems by providing reliable, scalable infrastructure for advanced analytics and AI-driven decision-making within a highly regulated retail environment.

What you'll do

  • Define and execute the data engineering strategy for the Retail analytics ecosystem.
  • Lead a large, multi-disciplinary engineering organization focused on building and scaling enterprise data platforms.
  • Design and deliver cloud-based data solutions to support retail pharmacy, supply chain, and marketing initiatives.
  • Partner with Data Science and Product teams to enable AI-driven decision-making and advanced analytics.
  • Drive platform modernization efforts and ensure alignment with enterprise technology strategies.
  • Manage the development of high-performing teams and mentor emerging leaders within the organization.
  • Translate complex business requirements into scalable technical roadmaps and engineering capabilities.

What we're looking for

  • Bachelor's degree in Computer Science, Engineering, Information Systems, Mathematics, Statistics, or a related quantitative field.
  • Master's degree in Computer Science, Engineering, Business Administration, Data Science, or a related discipline (preferred).
  • 15+ years of progressive experience in Data Engineering, Software Engineering, Analytics Engineering, or related technology disciplines.
  • 7+ years of experience leading large engineering organizations through multiple layers of leadership.
  • Proven experience designing and delivering enterprise-scale data platforms, data products, and cloud-native engineering solutions.
  • Expertise in modern data engineering practices including pipelines, distributed processing, cloud platforms, governance, and reliability.
  • Experience supporting Retail, Pharmacy, Merchandising, Supply Chain, Marketing, Consumer, or Omnichannel analytics environments (preferred).
  • Experience building real-time data solutions, feature stores, or enabling AI/ML through robust data engineering (preferred).

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