Pay
$127K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
United States
Live, first seen by us 5 days agoLocation type: RemoteFrom the employer’s posting
Employment type: Full-timeFrom the employer’s posting
Department: DataFrom the employer’s posting
Are you authorized to work in the United States without the need for visa sponsorship?From the employer’s application form
As a Staff Data Engineer, you'll sit within the data team and report to the VP of Data.
Our entire data warehouse, ETL, and reporting stack is built in Python on AWS, so deep Python experience is essential.
Define and own data platform architecture, technical standards and coding practices
Design, build and maintain Python ETL/ELT pipelines on AWS Glue
Drive analytical data models and the semantic layer for BI and analytics
Write and optimize complex SQL and database objects in Redshift
Own data quality, governance and reliability, including monitoring and incident response
As a Staff Data Engineer, you'll sit within the data team and report to the VP of Data. You'll own the data platform that powers the business's current data needs and help shape where it goes next.
Our entire data warehouse, ETL, and reporting stack is built in Python on AWS, so deep Python experience is essential. You'll write production-quality Python and SQL, set the architecture and technical standards the team builds on, and stay comfortable working hands-on with AWS services (e.g., Lambda, Glue) and BI tools when needed.
Define and own the architecture, technical standards, and coding practices for the data platform.
Design, build, and maintain scalable ETL/ELT pipelines in Python using AWS Glue.
Drive the analytical data models and semantic layer that power self-service BI and advanced analytics.
Author, optimize, and maintain complex SQL and database objects (e.g., views, stored procedures) in Redshift.
Own data quality, governance, and reliability for the platform, including monitoring, alerting, and incident response.
10+ years of professional experience in data engineering or a hybrid data/software engineering role.
Expert-level Python: deep experience architecting production data pipelines, ETL logic, and reporting systems. Python is our primary language, but we value engineers whose depth extends to other languages relevant to modern data engineering (Java, Scala, Go, etc.) and who can make sound tooling decisions, not just execute in one stack.
Hands-on experience with cloud data services, ideally AWS (e.g., Glue, S3, Redshift, Athena), including building and operating production data pipelines on them.
Experience handling sensitive data, such as PHI, with appropriate access controls and compliance practices.
Solid understanding of data modeling techniques: star/snowflake schemas, slowly changing dimensions, event-based models.
Demonstrated experience setting technical direction and standards that other engineers and teams build on.
Experience in health tech, digital health, or another regulated industry.
Experience with CI/CD for data assets (dbt CI, Great Expectations, or similar).
Prior experience in a startup or high-growth environment, owning outcomes end to end.
Experience with orchestration tools and platforms such as Apache Spark, Airflow, or equivalent.
9amHealth is an AI-enabled virtual specialty care platform focused on managing high-cost chronic conditions at scale.
In the employer’s words · Read in context · Company website ↗
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$127K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
Day 5
Similar jobs usually come down between day 5 and day 54.
From 10,704 similar postings that came down. Coming down is not always a hire.
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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- Strong data or software engineering background: able to write, test, debug, and ship production code independently, using practices like code review and CI/CD. - Expert-level Python: deep experience architecting production data pipelines, ETL logic, and reporting systems. Python is our primary language, but we value engineers whose depth extends to other languages relevant to modern data engineering (Java, Scala, Go, etc.) and who can make sound tooling decisions, not just execute in one stack. - Expert-level SQL: window functions, CTEs, recursive queries, and query optimization at scale.
As a Staff Data Engineer, you'll sit within the data team and report to the VP of Data. You'll own the data platform that powers the business's current data needs and help shape where it goes next. Our entire data warehouse, ETL, and reporting stack is built in Python on AWS, so deep Python experience is essential. You'll write production-quality Python and SQL, set the architecture and technical standards the team builds on, and stay comfortable working hands-on with AWS services (e.g., Lambda, Glue) and BI tools when needed. What It Is
- Evaluate and recommend data technologies, and contribute to the platform roadmap, including performance and cost optimization. - Design, build, and maintain scalable ETL/ELT pipelines in Python using AWS Glue. - Drive the analytical data models and semantic layer that power self-service BI and advanced analytics.
- Set the technical bar for AI-assisted development practices across the data team (GitHub Copilot, Claude, Cursor, or similar). - Author, optimize, and maintain complex SQL and database objects (e.g., views, stored procedures) in Redshift. - Operate and optimize AWS data infrastructure (e.g., Glue, S3, Redshift, Athena).
- Expert-level Python: deep experience architecting production data pipelines, ETL logic, and reporting systems. Python is our primary language, but we value engineers whose depth extends to other languages relevant to modern data engineering (Java, Scala, Go, etc.) and who can make sound tooling decisions, not just execute in one stack. - Expert-level SQL: window functions, CTEs, recursive queries, and query optimization at scale. - Hands-on experience with cloud data services, ideally AWS (e.g., Glue, S3, Redshift, Athena), including building and operating production data pipelines on them.
- Author, optimize, and maintain complex SQL and database objects (e.g., views, stored procedures) in Redshift. - Operate and optimize AWS data infrastructure (e.g., Glue, S3, Redshift, Athena). - Own data quality, governance, and reliability for the platform, including monitoring, alerting, and incident response.
- Expert-level SQL: window functions, CTEs, recursive queries, and query optimization at scale. - Hands-on experience with cloud data services, ideally AWS (e.g., Glue, S3, Redshift, Athena), including building and operating production data pipelines on them. - Production experience with a cloud data warehouse, ideally Amazon Redshift, including schema design and performance tuning.
Preferred Qualifications - Professional-level cloud certification (e.g., AWS Solutions Architect – Professional, Google Cloud Professional Data Engineer, or Azure Solutions Architect Expert), or equivalent hands-on experience. - Experience in health tech, digital health, or another regulated industry.
- Experience in health tech, digital health, or another regulated industry. - Experience with CI/CD for data assets (dbt CI, Great Expectations, or similar). - Background building or contributing to AI/ML features: feature stores, training pipelines, model serving, or RAG architectures.
- Background 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, owning outcomes end to end.
- Experience with orchestration tools and platforms such as Apache Spark, Airflow, or equivalent. - Familiarity with BI platforms and semantic layers, such as Looker/LookML, in support of self-service analytics. What you can expect at 9amHealth
- Hands-on experience with cloud data services, ideally AWS (e.g., Glue, S3, Redshift, Athena), including building and operating production data pipelines on them. - Production experience with a cloud data warehouse, ideally Amazon Redshift, including schema design and performance tuning. - Experience handling sensitive data, such as PHI, with appropriate access controls and compliance practices.
- A track record of improving developer experience and productivity through tooling, automation, or process improvements, and of influencing engineering practice beyond your immediate team. - Experience with orchestration tools and platforms such as Apache Spark, Airflow, or equivalent. - Familiarity with BI platforms and semantic layers, such as Looker/LookML, in support of self-service analytics.
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Yes. We’ve followed one other Staff Data Engineer job there since June 2026. It came down after 149 days.
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
9amHealth is an AI-enabled virtual specialty care platform focused on managing high-cost chronic conditions at scale. The company partners with employers, health plans, and pharmacy benefit managers to deliver comprehensive, cost-effective medical care for individuals living with obesity, diabetes, hypertension, and dyslipidemia. Members receive access to specialized clinicians, including endocrinologists, obesity medicine specialists, and clinical pharmacists, at-home lab testing, prescription medications, and lifestyle support.
9amHealth was founded in 2021 and is backed by leading healthcare investors like Define Ventures, SemperVirens, 7Wire Ventures, and The Cigna Group Ventures.
As a Staff Data Engineer, you'll sit within the data team and report to the VP of Data. You'll own the data platform that powers the business's current data needs and help shape where it goes next.
Our entire data warehouse, ETL, and reporting stack is built in Python on AWS, so deep Python experience is essential. You'll write production-quality Python and SQL, set the architecture and technical standards the team builds on, and stay comfortable working hands-on with AWS services (e.g., Lambda, Glue) and BI tools when needed.
Define and own the architecture, technical standards, and coding practices for the data platform.
Evaluate and recommend data technologies, and contribute to the platform roadmap, including performance and cost optimization.
Design, build, and maintain scalable ETL/ELT pipelines in Python using AWS Glue.
Drive the analytical data models and semantic layer that power self-service BI and advanced analytics.
Build integrations between the data platform and internal or third-party systems, contributing backend code where needed.
Set the technical bar for AI-assisted development practices across the data team (GitHub Copilot, Claude, Cursor, or similar).
Author, optimize, and maintain complex SQL and database objects (e.g., views, stored procedures) in Redshift.
Operate and optimize AWS data infrastructure (e.g., Glue, S3, Redshift, Athena).
Own data quality, governance, and reliability for the platform, including monitoring, alerting, and incident response.
Support AI/ML initiatives by partnering with data science and ML teams on data prep, feature engineering, and model monitoring.
Raise the team's technical standards directly, through code review, architecture decisions, and hands-on mentorship of the data analyst and data engineer.
Partner with clinical operations, product, finance, and engineering to translate ambiguous business questions into reliable, well-documented data products.
10+ years of professional experience in data engineering or a hybrid data/software engineering role.
Strong data or software engineering background: able to write, test, debug, and ship production code independently, using practices like code review and CI/CD.
Expert-level Python: deep experience architecting production data pipelines, ETL logic, and reporting systems. Python is our primary language, but we value engineers whose depth extends to other languages relevant to modern data engineering (Java, Scala, Go, etc.) and who can make sound tooling decisions, not just execute in one stack.
Expert-level SQL: window functions, CTEs, recursive queries, and query optimization at scale.
Hands-on experience with cloud data services, ideally AWS (e.g., Glue, S3, Redshift, Athena), including building and operating production data pipelines on them.
Production experience with a cloud data warehouse, ideally Amazon Redshift, including schema design and performance tuning.
Experience handling sensitive data, such as PHI, with appropriate access controls and compliance practices.
Solid understanding of data modeling techniques: star/snowflake schemas, slowly changing dimensions, event-based models.
Demonstrated experience setting technical direction and standards that other engineers and teams build on.
Professional use of AI-assisted development tools, with sound judgment on correctness, security, and review.
Ability to explain technical trade-offs and risks clearly to both technical and non-technical stakeholders, including executives.
Professional-level cloud certification (e.g., AWS Solutions Architect – Professional, Google Cloud Professional Data Engineer, or Azure Solutions Architect Expert), or equivalent hands-on experience.
Experience in health tech, digital health, or another regulated industry.
Experience with CI/CD for data assets (dbt CI, Great Expectations, or similar).
Background 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, owning outcomes end to end.
A track record of improving developer experience and productivity through tooling, automation, or process improvements, and of influencing engineering practice beyond your immediate team.
Experience with orchestration tools and platforms such as Apache Spark, Airflow, or equivalent.
Familiarity with BI platforms and semantic layers, such as Looker/LookML, in support of self-service analytics.
At 9amHealth, you'll join the exciting environment of a startup with the stability of highly experienced founders that have successfully built a business from scratch. We foster a collaborative learning mindset and a passion for improvement. You'll work with a brand-new product alongside a small, flexible team of people eager to shape technology, infrastructure, and culture.
Our team members are spread across the globe, with physical sites in San Diego, California, and Vienna, Austria, but we value your culture regardless of where you work.
Additionally, we provide a professional development budget and support continuing education, empowering our team to grow alongside the company. Team members have the opportunity to make a meaningful impact on chronic disease management and patient outcomes while contributing to a collaborative, mission driven culture focused on improving healthcare accessibility and affordability.
Full-Time/W2 Positions Only: We offer a comprehensive benefits package, including health, dental, and vision insurance, along with flexible PTO and work from home options.
Diabetes disproportionately impacts BIPOC (black, indigenous, and people of color) communities. We are committed to creating a workforce that reflects our patients and creating a positive healthcare experience for them. We welcome applications from people of color, LGBTQ+ individuals, people with disabilities, members of ethnic minorities, and foreign born residents.
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United States
What You Need - 10+ years of professional experience in data engineering or a hybrid data/software engineering role. - Strong data or software engineering background: able to write, test, debug, and ship production code independently, using practices like code review and CI/CD.
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