Data Engineer
New
5
540Federal health data
Remote within the continental United States.Full-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 3+ years of data engineering, software engineering, or related technical experience
- Required Skills
- PythonSQLGitSparkRESTful APIsData modelingDatabricks
Requirements
- Have 3+ years of data engineering, software engineering, or related technical experience.
- Demonstrate strong proficiency with Python and SQL.
- Have experience with Apache Spark or similar distributed data-processing frameworks.
- Have experience with Databricks or a similar modern data platform.
- Have experience building and maintaining production data pipelines and ETL/ELT processes.
- Understand data modeling and transforming raw data into reusable analytics datasets or data products.
- Have experience integrating or consuming data through REST APIs.
- Have experience troubleshooting and optimizing data pipelines for performance and reliability.
- Be familiar with data validation, quality, governance, security, privacy, and compliance considerations.
- Have experience with Git-based development workflows and modern software engineering practices.
- Be comfortable working in terminal and command-line environments.
- Have experience using AI-assisted development tools for engineering tasks, testing, documentation, and debugging.
- Hold a bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field (preferred).
Responsibilities
- Design, develop, and maintain scalable, production-ready data pipelines using Spark, Python, and SQL in a Databricks environment.
- Integrate and transform health, financial, and operational datasets into reusable data products.
- Develop data models and analytics datasets for enterprise reporting and operational decision-making.
- Build and maintain API-based integrations for ingesting and exchanging data across systems.
- Monitor, troubleshoot, and optimize scheduled workflows and production pipelines.
- Implement data validation, quality checks, lineage, and governance practices.
- Contribute to automated testing, code reviews, technical documentation, and reusable engineering patterns.
- Collaborate with engineers, architects, analysts, and customer stakeholders to translate data needs into technical solutions.
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