Senior Data Engineer

9amHealth

Senior Data Engineer at 9amHealth responsible for evolving the data platform. Focus on managing pipelines, data models, and AI initiatives for chronic disease management.

Posted 5/13/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

AirflowAmazon RedshiftApacheAWSETLJavaMySQLPythonSparkSpringSpring BootSpringBootSQLTableau

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Health, dental, and vision insurance
Flexible PTO and work from home options
Professional development budget
Support for continuing education

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonSQLETLdata pipelinesdata modelingreporting systemsAPI integrationsdata transformationsAI/ML featuresdata quality frameworks

Soft Skills

collaborationmentoringcommunicationproblem-solvingleadershipdocumentationcode revieworganizational skillsanalytical thinkingteam standards