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# Senior Analytics Engineer

- **Company:** Health Union
- **Location:** Philadelphia, PA, United States
- **Experience:** Expert
- **Salary:** $95,000 - $120,000
- **Contract:** Permanent contract
- **Skills:** Sql Data Warehouse, Artificial Intelligence, Data Analysis, Code Review, Data Warehousing, Decision Support Systems, Raw Data, Scripting, Sql Optimization, Snowflake, Data Management, Claude
- **Published:** November 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c366b9e092483e8b

## About the Role

* 5+ years of experience in analytics engineering or a similar role
 * Advanced SQL skills in a cloud data warehouse environment; Snowflake experience preferred
 * Advanced dbt proficiency, especially in testing, documentation, and model design best practices
 * Deep knowledge of relational and dimensional data modeling, as well as data warehouse and BI system design principles
 * Proven ability to translate business questions and stakeholder needs into data models and reporting solutions
 * Experience with, or strong instincts for, data quality monitoring and observability
 * Hands-on use of AI tools, both to accelerate development and to solve business problems directly
 * Clear communication with technical and non-technical teams alike
 * Self-direction and strong organizational skills, with the ability to manage your own priorities

Nice to Have

 * Experience building semantic models or working with a semantic layer
 * Python scripting experience
 * Background in health, pharmaceutical, or online publishing data
 * Familiarity with Kanban or similar lightweight delivery frameworks

## Description

At Health Union, data is the foundation of how we understand our business and serve our communities. Our Analytics Engineering team turns raw data into trusted insights for decision-makers across the organization, working in a modern stack built on dbt and Snowflake, along with AI tools like Claude., We're looking for a Senior Analytics Engineer to own work end to end: translating business requirements into robust, well-documented data models, then turning those models into intuitive AI-driven tools that stakeholders rely on. You'll also partner with our Data Platforms team on everything from ingestion to transformation readiness, including building and evolving the data quality monitoring that catches issues before they reach a dashboard.

This is a highly collaborative role. You'll work closely with business analysts and stakeholders across the organization to understand their needs and deliver data they can trust. If you enjoy pairing technical rigor with cross-functional partnership, we'd love to hear from you.

What You Do:

Data Modeling & Transformation

 * Design, build, and maintain dbt models that power business intelligence and decision support across Health Union
 * Translate business requirements from analysts and stakeholders into well-structured, documented, and tested transformation logic
 * Partner with the Data Platforms team to define what data is extracted and loaded from source systems, and confirm it's structured for clarity and usability
 * Contribute to architectural and design decisions with your team and manager

Semantic Layer Development

 * Build and maintain semantic models, including migrating existing BI data models into the semantic layer
 * Make sure AI tools generate accurate queries from natural-language business questions
 * Partner with analysts and stakeholders to confirm that AI-driven BI tools meet their analytic needs

Data Quality

 * Enhance our existing data quality monitoring so issues are caught before they reach business stakeholders
 * Build on our current dbt tests to design a more robust testing framework
 * Establish processes and standards that improve the reliability of data across the business

Collaboration & Issue Resolution

 * Serve as a senior technical resource for analysts and stakeholders, helping diagnose and resolve data issues
 * Raise the team's technical bar through code review, documentation, and knowledge sharing
 * Manage and prioritize work transparently within the team's Kanban process