Data Engineer II
Builds and maintains scalable data pipelines, warehouse transformations, and visualizations using Snowflake, AWS, SQL, Python, and dbt to support public health programs. Requires 2-5 years data engineering experience and expertise in cloud data platforms.
Responsibilities
- Contribute to the technical integrity and evolution of the Data Platform tech stack, working closely with other Data Engineers, the Director of Technology, and the USS Tech Lead.
- Design and implement core features and enhancements within the Data Platform, including contributing to technical specifications, conducting targeted technical research, and translating requirements into production-ready solutions.
- Execute and maintain DevOps workflows supporting the Data Platform, including performance monitoring, platform upgrades, deployment frameworks, and operational improvements.
- Use AI-assisted tools to accelerate development, debugging, documentation, and operational analysis, while validating outputs for correctness, reliability, and security.
- Build and maintain robust data extraction, loading, and transformation processes for Dimagi managed (i.e. CommCare) and external data sources using SQL and Python scripting.
- Design and develop data warehouse transformations using SQL-based approaches and tools such as dbt.
- Collaborate with internal teams and external partners on the design and implementation of enterprise data architectures.
- Conduct ad hoc analyses and support the development of business intelligence outputs, including dashboards and visualizations using Tableau and other tools.
Requirements
- 2–5 years of experience in data engineering or similar technical role, with proven track record of designing and evolving scalable data systems.
- Experience building maintainable, long-term technical solutions using software development best practices (version control, testing, iterative development).
- Hands-on expertise in building and managing production-grade pipelines using ETL/ELT tools (e.g., dbt, Airflow, Prefect, Fivetran, or Talend).
- Strong proficiency with cloud-based data platforms (AWS, Snowflake) and diverse data ingestion, processing, and storage technologies.
- Expert-level SQL for complex data engineering and analysis, paired with proficiency in Python and associated data-oriented toolkits.
- Deep understanding of dimensional modeling concepts (e.g. OLAP cubes, star schemas, Kimball architecture).
- Proven ability to partner with technical stakeholders to clarify requirements and deliver effective data solutions.
- Proficiency in using AI-assisted tools for code generation, debugging, and optimization.
- Comfortable working on production systems to test, iterate, and optimize operational workflows.
- Eligible to work in the United States.
Nice-to-Haves
- Experience in enterprise data architecture, service-oriented frameworks, data integration, data strategy and governance, data lakes, data operations.
- Experience writing production-ready code in Python, Java, C++.
- Experience with data analysis software (Jupyter Notebooks, R) and visualization tools (Tableau, Power BI, Superset).
- Healthcare or public health data experience.
- Experience working independently with partners in agile environments using JIRA and Asana.
Compensation
- Estimated salary range: $82,810 - $130,319 USD annually (cost of living adjusted, non-negotiable).
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