ETL Developer - US Healthcare Experience

Interscripts, Inc.•Chantilly, VA

6d•Onsite

About The Position

We are looking for a skilled and detail-oriented ETL Developer to join our growing data engineering team. In this role, you will design, develop, test, and optimize ETL processes that support enterprise data integration, migration, and warehousing initiatives. The ideal candidate should have strong experience in SQL, ETL development, and data transformation, along with exposure to modern AI-assisted development tools and automation practices. Experience working with US Healthcare data environments is highly preferred.

Requirements

Bachelor’s degree in Computer Science, Information Technology, or related field.
3–5 years of hands-on ETL development experience.
Strong experience with ETL tools such as Talend or SSIS.
Strong SQL skills and experience with databases such as SQL Server, Oracle, MySQL, or Azure SQL.
Experience with cloud and on-prem data environments.
Familiarity with Git and CI/CD deployment practices.
Experience using AI coding assistants or automation tools in development workflows.
Strong analytical, troubleshooting, and problem-solving abilities.
Excellent communication and collaboration skills.
Ability to work independently in a fast-paced environment.
Exposure to US Healthcare data standards such as HL7, FHIR, EDI 837/835, and claims processing.
Understanding of HIPAA compliance and secure healthcare data handling practices.

Nice To Haves

Experience with Azure Data Factory, AWS Glue, Databricks, or similar cloud ETL platforms.
Knowledge of Python or Spark for large-scale data transformations.
Familiarity with data governance and metadata management tools.
Exposure to AI/ML data pipelines or MLOps workflows.
Healthcare domain experience is a plus.

Responsibilities

Design, develop, test, and maintain ETL workflows and data pipelines.
Integrate data from multiple on-prem and cloud-based systems.
Develop and maintain data models, mappings, and data dictionaries.
Monitor ETL jobs, troubleshoot failures, and resolve data quality issues.
Collaborate with analysts, business teams, and stakeholders to understand integration requirements.
Ensure data accuracy, consistency, completeness, and scalability across pipelines.
Maintain technical documentation for ETL processes and data architecture.
Implement best practices for ETL deployment, version control, and CI/CD.
Utilize AI-assisted coding and automation tools to improve development efficiency and debugging.
Support continuous improvements in data quality, performance, and operational reliability.
Stay current with emerging ETL, cloud, and AI-driven data engineering technologies.