Data Engineer: Build Scalable Pipelines & Data Quality

MedWatchers; San Diego (CA); On-site; USD 100,000 - 150,000; Full time

Job summary

MedWatchers is seeking a Data Engineer to own the production data pipelines behind client programs in our California-based team. You will diagnose issues, automate recurring data tasks, and partner with program teams to design scalable data solutions.

The role emphasizes strong Python ETL skills, SQL, and cloud data work in AWS, plus experience with Docker, Airflow/Prefect/Dagster, and data quality tooling to improve reliability and reduce manual requests over time.

Qualifications

• 3+ years in data engineering or similar role with production pipelines.
• Experience building cloud data solutions in AWS.
• Strong Python for ETL/ELT with maintainable, testable code.
• Advanced SQL and data modeling.
• Docker in production environments.
• Workflow orchestration with Airflow, Prefect, or Dagster.
• Data validation/quality frameworks like Great Expectations or dbt tests.
• Experience with data catalogs or metadata tools is a plus.

Responsibilities

• Owning, maintaining, and improving the production data pipelines behind client programs.
• Diagnosing and resolving pipeline issues, and hardening the systems that cause them.
• Turning recurring, high-touch data work into automated processes that run on their own.
• Partnering with program teams on client data and reporting needs, and designing systems that reduce those requests over time.
• Extending our data validation framework so quality issues surface earlier and more visibly

Skills

Data Eng Exp

AWS

Python

ETL/ELT

SQL

Docker

Airflow

Prefect

Dagster

Great Expectations

dbt tests

MongoDB

DataHub

OpenMetadata

Kubernetes

Job description

MedWatchers is seeking a Data Engineer to own the production data pipelines behind client programs in our California-based team. You will diagnose issues, automate recurring data tasks, and partner with program teams to design scalable data solutions.

The role emphasizes strong Python ETL skills, SQL, and cloud data work in AWS, plus experience with Docker, Airflow/Prefect/Dagster, and data quality tooling to improve reliability and reduce manual requests over time.