Software Development Engineer - Front Store

CVS Health•New York, NY

1d•$86,520 - $173,040•Onsite

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. About the Team The Retail Analytics team supports CVS Health’s Front Store business by delivering data-driven business product that improve consumer experience a ess performance. We are a cross-functional, agile team working at the forefront of analytics and AI/ML technology for key CVS Retail strategic priorities. Within our team, we believe a cutting-edge product can only be delivered if every aspect of the solution — from data model to front-end UI — is fully designed and developed by SMEs within the team. Role Overview As a Software Development Engineer on the Retail Analytics team, you will be primarily responsible for designing and building the data foundation that powers our analytics platform. This means owning the end-to-end data layer in Snowflake — fact tables, dimension tables, and aggregate metrics tables — and building the ThoughtSpot data models and TML (ThoughtSpot Modeling Language) that expose that data to business users. You will also contribute to the React/TypeScript front-end layer, embedding ThoughtSpot analytics experiences into our internal applications. This is an individual contributor role with opportunity to grow technical scope and platform ownership over time.

Requirements

3+ years of hands-on software or data engineering experience
2+ years of SQL development — strong command of complex queries, joins, window functions, CTEs, and query optimization in a cloud data warehouse (Snowflake preferred)
Demonstrated experience designing dimensional data models — facts, dimensions, and aggregate/summary tables
Experience building or maintaining BI semantic layers, data models, or governed metrics definitions (ThoughtSpot, dbt, Looker, or equivalent)
1+ years of React and TypeScript development — able to build and maintain front-end components and integrate third-party SDKs
Experience with cloud-based platforms (Azure, GCP, or AWS)
Strong communication skills and ability to collaborate with analysts, product managers, and business stakeholders

Nice To Haves

Direct experience with ThoughtSpot — Worksheets, Liveboards, TML, and the ThoughtSpot Everywhere SDK
Experience with Snowflake-specific features: clustering keys, materialized views, dynamic tables, and cost governance
Familiarity with retail merchandising domain concepts (pricing, assortment, promotions, category management)
Experience working in an agile/scrum environment

Responsibilities

Design and build Snowflake fact, dimension, and aggregate metrics tables that support retail analytics use cases including pricing, assortment, promotions, and category management
Develop and maintain ThoughtSpot data models — Worksheets, Views, and TML definitions — to expose curated, governed data layers to business users
Write and optimize complex SQL in Snowflake including window functions, CTEs, incremental loading patterns, and query performance tuning
Define and enforce data modeling standards, naming conventions, metric definitions, and documentation practices across the analytics data layer
Integrate ThoughtSpot analytics experiences (liveboards, search, visualizations) into internal applications using the ThoughtSpot Everywhere SDK with React and TypeScript
Build and maintain React/TypeScript components that wrap and extend ThoughtSpot embedded experiences, connecting them to Snowflake-backed APIs and data models
Work closely with cross-functional teams, including PMs, Data Engineering, and business stakeholders, to identify business needs and translate them into requirements for solution improvements or ad hoc analyses in a fast-paced environment.