Principal Data Engineer

Devoted Health•Waltham, MA

12d•$131,000 - $200,000

About The Position

Devoted Health’s Data Platform team designs and builds infrastructure that supports analytics, machine learning, and AI workloads. The Principal Data Engineer will develop tools and platform capabilities that help Data Scientists and Engineers build and deploy scalable, reliable data pipelines, machine learning models, and LLM-powered tools. The platform uses technologies including Snowflake, Apache Airflow, dbt, Kubernetes, Terraform, and AI systems; most internal tools are written in Go. The role includes developing infrastructure for a Semantic Layer and Data Agent that help users discover and understand data, ask questions in plain language, and safely query data to build dashboards and analyses. The engineer will establish evaluation, observability, and quality practices for AI systems that rely on the platform, collaborate with Data Science and Product Engineering, and lead platform improvements from design through implementation. The team values pragmatism, performance, reliability, maintainability, collaboration, knowledge sharing, and continuous improvement. It uses AI coding agents in its everyday workflow. Compensation is listed at $131,000–$200,000, plus cash bonus and equity. The organization’s total rewards package includes employer-sponsored health, dental, and vision plans; paid time off; a monthly mobile or internet stipend; stock options; bonus eligibility; parental leave; and a 401(k) program. The total rewards package is for full-time employees only.

Requirements

Ability to work in a fast-paced startup environment.
At least 5 years working as an individual technical contributor.
At least 5 years of experience with data platforms.
Experience with cloud data warehouses such as Snowflake, BigQuery, or Redshift.
Experience developing in Python or Go.
Experience with a workflow orchestrator such as Airflow, Luigi, Dagster, or Prefect.
Strong understanding of data warehousing and data modeling principles.
Experience with semantic or metrics layers such as dbt Semantic Layer, Cube, or LookML.

Nice To Haves

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

Responsibilities

Design, develop, and maintain a robust, scalable data platform for analytics, machine learning, and AI workloads.
Build and evolve infrastructure behind the Semantic Layer and Data Agent, including how LLMs discover, understand, and safely query data.
Establish evaluation, observability, and quality practices for AI systems that depend on the data platform, measuring accuracy and catching 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 in the platform.
Propose new or improved solutions and lead them from design through implementation.
Develop best practices for data modeling, pipelines, and testing.