Senior Data Engineer

9amHealth is an AI-enabled virtual specialty care platform focused on managing high-cost chronic conditions at scale. They are seeking a Senior Data Engineer to own and evolve the data platform that powers clinical operations, business intelligence, and AI initiatives.

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

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
• 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