Note: The job is a remote job and is open to candidates in USA. Pinnacle Talent Placement is a mid-to-large healthcare technology company focused on Revenue Cycle Management solutions. They are seeking a Data Engineer to build and maintain production pipelines for their cloud-native data platform, involving migration from legacy systems and ensuring data quality and compliance with HIPAA regulations.
Responsibilities
- Build and maintain ETL/ELT pipelines for healthcare data ingestion and transformation across assigned client environments
- Monitor daily ETL job execution across all active feeds — resolve failures, missing files, and data quality issues independently before they impact revenue collections or payment posting
- Evaluate inbound flat file structure, completeness, and data quality to determine the appropriate ETL configuration and mapping approach
- Design and implement mapping engines and processes for inbound claims (837I/837P), remittance (835), claims status (277), and supplemental payer files — translating file specs into production-ready ingestion logic
- Define and apply mapping rules for new and modified feed configurations
- Implement data quality checks and validation logic to ensure accuracy and completeness of claims, remittance, and status data
- Serve as primary ETL contact for internal teams and external payers — delivering timely status updates to billing ops, revenue cycle leadership, and technical stakeholders
- Write, test, and optimize SQL transformation logic for high-volume healthcare datasets
- Support synchronization of data between operational data stores (ODS), data warehouses, and analytical platforms
- Enforce PHI/HIPAA controls throughout all data processing work
- Assist with migration tasks as the team transitions legacy ETL workflows to Databricks and Azure
- Document pipeline logic, data flows, and known edge cases for team knowledge continuity
- Participate in code review — giving and receiving feedback on implementation quality
- Engage directly with revenue cycle operations, billing teams, or product managers to clarify and document data requirements when specs are incomplete or absent
- Translate business requirements into technical implementation plans for assigned pipeline work — escalating architectural decisions to senior engineers where scope warrants
Skills
- 2–5 years of hands-on experience in data engineering or a closely related technical role
- Bachelor's degree in Computer Science, Information Systems, Data Science, or equivalent practical experience
- Solid understanding of SQL — writes and debugs complex queries and transformations independently
- Hands-on experience with Python for data processing and automation tasks
- Proficiency in at least one ETL/ELT tool — Azure Data Factory, Databricks, SSIS, or equivalent
- Solid understanding of how data moves through ingestion, transformation, and loading stages end-to-end
- Hands-on experience with Azure SQL Database or SQL Server in a production environment
- Demonstrated ability to implement data quality checks and handle exception conditions in pipelines
- Comfortable working in Git-based version control workflows
- Hands-on experience with healthcare transaction formats — 837I/837P, 835, 277 — or ability to learn them quickly in context
- Working knowledge of file delivery processes including SFTP and PGP encryption, and experience monitoring ingestion status
- Working knowledge of healthcare billing form types — UB-04, CMS-1500 — and familiarity with inpatient/outpatient billing and payer adjudication workflows
- Solid understanding of HIPAA requirements and PHI handling expectations in a data engineering context
- Works independently on defined tasks without requiring daily direction
- Comfortable asking the right questions to turn vague business needs into concrete technical requirements — without waiting to be handed a spec
- Communicates blockers and trade-offs clearly to teammates and leads
- Attention to detail in code, data, and documentation
- Collaborative in code review — gives and receives feedback constructively
- Hands-on experience with Databricks, Delta Lake, or Spark
- Hands-on experience with health system source platforms — Epic, MEDITECH, Cerner, or Athena
- Familiarity with Medallion Architecture (Bronze / Silver / Gold pattern)
- Hands-on experience with SSIS, Informatica, or custom middleware integrations
- Comfortable with NoSQL databases such as MongoDB
- Exposure to CI/CD practices for data workflows
- Familiarity with dbt, Great Expectations, or similar data quality frameworks
- Azure Data Engineer Associate (DP-203) certification or working towards it
Company Overview