Data Engineer

CORMAC is a healthcare information technology company serving public healthcare programs through data management, analytics, and digital solutions. The Data Engineer will design, build, and operate Databricks and AWS data pipelines for Medicare claims, patient assessment, and provider demographic data, producing validated quality measure outputs and reporting files. The role also supports data validation, healthcare data security, provider matching, and preparation for HL7/FHIR standards.

Responsibilities:

• Design, build, and maintain scalable data pipelines in Databricks (Spark with Scala and/or PySpark, and SQL) that ingest, integrate, validate, and transform Medicare claims, patient assessment, and provider demographic data
• Develop, schedule, and optimize Databricks jobs, notebooks, and workflows, and tune Spark performance and cluster cost for large-scale claims processing
• Process Medicare claims and encounter data, including claim types, variables, values, and final action record logic
• Implement claims-based and hybrid quality measure specifications, including risk adjustment and imputation, using Databricks, SQL, PL/pgSQL, PostgreSQL, Python, SAS, and R as appropriate
• Build and integrate pipelines with AWS services such as S3, Glue, Lambda, SQS, EventBridge, EMR, Redshift and Redshift Spectrum, and RDS for PostgreSQL
• Produce confidential provider-level quality measure feedback reports and public reporting files on weekly, monthly, and quarterly cycles
• Apply matching rules across provider identifiers such as internal system provider IDs, CCNs, and NPIs, and geographic indicators, to track provider demographic changes such as address, certification, and ownership changes
• Implement patient and resident ID assignment, assessment matching, episode and stay building, and de-duplication routines
• Implement automated data validation frameworks, ETL/ELT testing suites, and statistical anomaly detection to validate measure logic, stored procedures, and output schemas before release
• Support relational schema and virtual data mart design for multi-dimensional, longitudinal analysis, and ensure de-identification and data security requirements are met for PII and PHI
• Prepare data pipelines for HL7/FHIR and digital quality measure data standards by applying the same core ingestion and validation logic

Requirements:

• Bachelor's degree in Computer Science, Information Technology, Statistics, Data Science, or a similar field
• Must be a U.S. Citizen
• Must be able to obtain a Public Trust (Tier I) Clearance
• Minimum of 7+ years of experience in data engineering, ETL/ELT development, or database development
• Minimum of 3+ years of hands-on experience building and operating production data pipelines in Databricks using Spark (Scala and/or PySpark) and SQL
• Advanced SQL skills and strong experience with PostgreSQL or another enterprise relational database, including stored procedures
• Hands-on experience with AWS data services such as S3, Glue, Lambda, and Redshift or RDS
• Experience validating data and outputs, including automated testing of ETL/ELT pipelines and data quality checks
• Experience working with large-scale healthcare data and protecting sensitive PII and PHI in accordance with HIPAA
• Strong communication skills with the ability to work in a cross-functional Agile team
• Master's degree in a relevant field
• Databricks certification (for example Databricks Certified Data Engineer Associate or Professional)
• Experience with Medicare and Medicaid claims data and CMS data assets
• Experience with CMS quality measure calculation, risk adjustment, and CMS quality reporting programs
• Experience with SAS and R, and with Snowflake
• Experience with provider data sources and provider identifier resolution (CCN, NPI)
• Experience with Amazon EMR and Redshift Spectrum
• Experience with HL7/FHIR data standards
• Experience producing public reporting files