Senior Data Engineer

9amHealth

1d•Remote

About The Position

9amHealth is seeking a Senior Data Engineer to join their Data & Analytics team. This role involves owning and evolving the data platform that supports clinical operations, business intelligence, and AI initiatives. It's a hands-on, full-stack data role requiring strong software engineering skills with a specialization in data. The position involves building and maintaining pipelines, designing analytical data models, contributing to backend application code, and adopting AI-assisted development practices. The ideal candidate will be proficient in Python and SQL, comfortable with AWS data services, and thrive in a fast-paced startup environment.

Requirements

10+ years of professional experience in data engineering, analytics engineering, or a hybrid data/backend software engineering role.
Strong software engineering background: this role requires someone who can write, test, debug, and ship production code, not just query data.
Expert-level Python: deep experience building production data pipelines, ETL logic, and reporting systems in Python.
Expert-level SQL: window functions, CTEs, recursive queries, query optimization, and performance tuning at scale.
Hands-on experience with AWS data services, specifically Glue, S3, Redshift, Athena, CloudFormation, CloudWatch, and IAM.
Experience with MySQL/Aurora in a production environment.
Hands-on experience building and operating data pipelines with AWS Glue, Spark, dbt, Airflow, or comparable frameworks.
Deep experience with at least one modern BI platform. Looker (LookML) strongly preferred; Tableau also valued. Should include semantic modeling, dashboard design, and self-service enablement.
Solid understanding of data modeling techniques: star/snowflake schemas, slowly changing dimensions, event-based models.
Familiarity with AI-assisted coding tools (GitHub Copilot, Claude Code, Cursor, Cody) and a demonstrated interest in integrating AI into engineering workflows.

Nice To Haves

Proficiency in Java (Spring Boot, Maven/Gradle) with experience shipping backend services or data-intensive applications to production.
AWS certifications (e.g., Solutions Architect, Data Analytics Specialty, or Database Specialty).
Experience in health tech, digital health, or regulated industries (HIPAA familiarity is a plus).
Experience with CI/CD for data assets (dbt CI, Great Expectations, or similar).
Background in building or contributing to AI/ML features: feature stores, training pipelines, model serving, or RAG architectures.
Comfort with infrastructure-as-code (Terraform, CloudFormation) and containerized deployments (Docker, ECS/EKS).
Prior experience in a startup or high-growth environment where you owned outcomes end to end.
Track record of improving developer experience and productivity through tooling, automation, or process improvements.

Responsibilities

Design, build, and maintain scalable data pipelines and ETL/ELT workflows in Python using AWS Glue, Apache Spark, Airflow, or equivalent orchestration tools.
Write production-grade Python code for DWH logic, reporting jobs, data transformations, and internal tooling, following software engineering best practices (testing, code review, CI/CD).
Develop and optimize analytical data models (dimensional, OBT, or hybrid) that serve self-service BI and advanced analytics use cases.
Build and maintain dashboards, explores, and semantic layers in Looker and/or Tableau; serve as the analytics infrastructure owner ensuring data quality and governance.
Contribute backend application code in Java (Spring Boot) or Python to support data-intensive features, API integrations, and internal services.
Champion modern AI coding practices across the data team, leveraging tools like GitHub Copilot, Claude, Cursor, or similar AI-assisted development environments to accelerate delivery and code quality.
Author and maintain comprehensive SQL assets (stored procedures, views, complex queries) across Redshift, Aurora/MySQL, and Athena.
Operate and optimize AWS data infrastructure including Glue, S3, Redshift, CloudFormation, CloudWatch, IAM, and Athena.
Collaborate closely with clinical operations, product, finance, and engineering teams to translate business questions into reliable, well-documented data products.
Implement data quality frameworks, monitoring, alerting, and incident response processes for the data platform.
Contribute to the architecture and data strategy for AI/ML features, including data prep, feature engineering, and model monitoring.
Mentor the existing data analyst and data engineer; help establish team standards, code review practices, and documentation norms.