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# Principal Data Engineer

- **Company:** Devoted Health
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $131,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Data Analysis, BigQuery, Cloud Database, Data Infrastructure, Data Warehousing, Cursor, Python (Programming Language), Machine Learning, OpenAI, SQL Databases, System Availability, Large Language Models, Claude Code, Snowflake, Change Data Capture, AI Coding Agents, Kubernetes, Luigi, Data Management, Terraform, Data Pipelines, Amazon Redshift
- **Published:** October 26, 2026
- **Apply:** https://startup.jobs/principal-data-engineer-devoted-com-10597378

## About the Role

* Experience coupled with a willingness to learn new things, especially in a fast-moving AI landscape
 * Comfort with ambiguity and a desire to be close to business problems
 * Pragmatism about AI. Excitement for what it can do, paired with clear thinking about where it isn't the right tool
 * Desire to work in a collaborative environment and improve as a team
 * Desire to contribute individually with the ability to self-direct work, * Ability to work in a fast-paced startup environment
 * 5+ years working as an individual technical contributor
 * 5+ years' experience with data platforms
 * Experience working with Cloud Data Warehouses (Snowflake, BigQuery, Redshift, etc.)
 * Experience developing in Python or Go
 * Experience with a Workflow Orchestrator (Airflow, Luigi, Dagster, Prefect, etc.)
 * A strong understanding of data warehousing and data modelling principles
 * Experience with semantic or metrics layers (dbt Semantic Layer, Cube, LookML, etc.)

Desired skills and experience:

 * Regular use of coding agents (Claude Code, Codex, Cursor, etc.) in your development workflow
 * Hands-on experience building or supporting applications that use LLMs (via Anthropic, OpenAI, or similar APIs)
 * Experience designing evaluations for LLM systems, such as text-to-SQL accuracy

## Description

In our mission to take care of every member like they were our own family,, we believe that data is a super power to deliver great service and clinical care. Our Data Platform team designs and builds the infrastructure to support this capability.

In this role, you will write tools to make it easier for our Data Scientists and Engineers to build and deploy scalable, reliable data pipelines, machine learning models, and LLM powered tools. You will help develop and improve a Data Platform built with tools like Snowflake, Apache Airflow, dbt, Kubernetes, Terraform, and all the leading AI labs. You will work on a wide range of impactful projects. Examples of our work include a Change-Data-Capture solution to stream data to Snowflake, a custom Snowflake SQL formatter, an internal Semantic Layer and Data Agent that lets users ask questions about our data in plain language and develop dashboards and analyses. We write most of our tools in Go.

You will be joining a team that values using the right tool for the job with an emphasis on performance, reliability, and maintainability. We also believe in continuously evaluating decisions and striving for improvement as a team. Additionally, we value sharing knowledge, eliminating silos, and collaboration. We use AI coding agents as part of our everyday workflow and are always looking for ways to work smarter.

Responsibilities will include:

 * Design, develop and maintain a robust, scalable data platform that serves analytics, machine learning, and AI workloads
 * Build and evolve the infrastructure behind our Semantic Layer and Data Agent, including how LLMs discover, understand, and safely query our data
 * Establish evaluation, observability, and quality practices for AI systems that depend on the data platform, so we can measure accuracy and catch regressions
 * Collaborate with Data Science and Product Engineering on tooling to develop and deploy reliable data and AI products
 * Ensure data availability, quality, and reliability in the data platform
 * Identify areas for improvement with the platform
 * Propose new/improved solutions and lead them from design to implementation
 * Develop best practices for data modelling, pipelines, and testing