Data Engineer — Healthcare Revenue Cycle

Data Engineer — Healthcare Revenue Cycle Status: Must be US Citizen, Green Card, or VISA that DOES NOT need sponsoring. We are not able to sponsor VISAs Resume must be in English Location: Remote Engagement: Contract, Long Term, Full Time Data Engineer — Healthcare Revenue Cycle Company Context Mid-to-large healthcare technology company focused on Revenue Cycle Management (RCM) solutions, operating in a multi-client environment with complex data integration requirements. The organization is actively modernizing legacy systems while maintaining operational stability for existing clients. Role Overview This role exists to build and maintain the production pipelines that power our cloud-native data platform — migrating from legacy ETL tooling and R-based processes to an architecture built on Databricks, Azure SQL Database, and Python. The Data Engineer works independently on well-scoped pipeline work: building ingestion and transformation logic, implementing data quality checks, and supporting payer integrations under defined specs. Work spans both greenfield platform development and active legacy system support, with HIPAA compliance and PHI handling requirements throughout. The downstream consumers of this work are revenue cycle operations teams whose billing and reconciliation workflows depend directly on data pipeline reliability and accuracy. Key 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 Required Qualifications Experience & Education • 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 Technical Skills • 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 Soft Skills • 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 Preferred Qualifications • 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 Team & Environment Collaboration Model • Distributed data engineering team of 5–10 engineers • Regular collaboration with senior engineers and tech leads on design decisions • Growing cross-functional exposure to product, analytics, and revenue cycle operations teams Work Style • Primarily hands-on: the expectation is significant time writing and testing pipeline code • Works within established architectural standards — raises questions before deviating • Documentation and knowledge-sharing are explicit team expectations, not afterthoughts Technical Environment • Azure as primary cloud platform, with hybrid architecture supporting both modern and legacy patterns • Multiple simultaneous client deployments at varying levels of technical maturity Growth Opportunities • Clear path to Senior Data Engineer as scope of ownership and architectural decision-making expands • Exposure to Medallion Architecture, Delta Lake, and modern RCM data platform design • Opportunity to specialize in healthcare data formats, payer integrations, or data quality engineering