Data Engineer — Revenue Cycle & Healthcare Payer Systems

Our client is a healthcare technology consultancy delivering data engineering and analytics solutions to payer and provider organizations across the US. They are seeking a skilled Data Engineer with hands-on revenue cycle management experience to build, deploy, and maintain data pipelines, integrations, and EDW solutions across a portfolio of client engagements — spanning health plans, hospital systems, physician groups, and RCM technology vendors. Core responsibilities: • Build and maintain production-grade ELT/ETL pipelines that ingest, parse, and transform X12 EDI, HL7 FHIR, and flat-file RCM data from payer and provider source systems • Implement data models designed by the architecture team in cloud data platforms (Snowflake, Azure Synapse, Redshift, or BigQuery), including dbt model development and testing • Develop and maintain integrations between RCM platforms (clearinghouses, EHRs, billing systems) using APIs, SFTP workflows, and message queue patterns • Build data quality frameworks — validation rules, reconciliation checks, and anomaly alerts — specific to RCM transaction volumes and payer contract logic • Implement HIPAA-compliant data handling including PHI masking, de-identification, audit logging, and role-based access controls across pipeline and storage layers • Support client-facing delivery by documenting pipelines, producing data lineage artefacts, and participating in architecture and QA reviews • Contribute to internal accelerators — reusable pipeline templates, X12 parsers, FHIR transformers — that reduce time-to-deliver across client engagements Required skills: • 5+ years in data engineering with at least 3 years in healthcare — hands-on experience working with both payer and provider RCM data strongly preferred • Proficiency in Python and SQL for pipeline development; experience with dbt (Core or Cloud) for transformation layer implementation • Solid working knowledge of X12 EDI transaction sets (837P/I, 835, 834, 270/271, 276/277) and HL7 FHIR R4 — able to parse, validate, and transform these formats without scaffolding • Hands-on cloud data platform experience on Snowflake, Azure Synapse, Redshift, or BigQuery; understanding of warehouse optimization, partitioning, and cost management • Experience with orchestration tools (Apache Airflow, Azure Data Factory, Prefect, or Dagster) and CI/CD pipelines for data engineering (GitHub Actions, Azure DevOps) • Familiarity with RCM source systems — Epic Resolute, Cerner RevElate, athenahealth, Waystar, Change Healthcare, or major clearinghouse file formats • Working knowledge of HIPAA requirements as applied to data pipelines — PHI identification, de-identification Safe Harbor / Expert Determination, BAA context Preferred platforms & tooling: • Cloud: Snowflake, Azure (ADF, Synapse, FHIR Service), AWS, GCP • Orchestration: Airflow, Azure Data Factory, Prefect, or Dagster • Transformation: dbt Core or dbt Cloud • EHR / PMS: Epic, Oracle Cerner, athenahealth, eClinicalWorks • Clearinghouses: Waystar, Change Healthcare, Availity • Version control & CI/CD: Git, GitHub Actions, Azure DevOps Nice to have • Experience with Spark or distributed processing for high-volume claims datasets • FHIR-native pipeline patterns using Azure FHIR Service or AWS HealthLake • Exposure to Da Vinci FHIR IGs (CDex, PCDE, PDex) for payer data exchange • Value-based care or APM contract data pipeline experience • RPA tooling (UiPath, Automation Anywhere) in RCM automation contexts • CMS price transparency or No Surprises Act data handling experience