Data Product Engineer

MMC Group is hiring a project based Data Product Engineer to join our client on a multi-year enterprise data platform program for a large healthcare organization they're supporting. In this role, you will help design, build, and operate reusable data products on a Databricks lakehouse, turning clinical and operational source data into governed, trusted assets that business, clinical, and research teams can use with confidence. The program is building a next-generation enterprise data platform for a research-intensive healthcare organization. The work spans several connected efforts: A foundational enterprise data model and Lakehouse built on Databricks. A portfolio of reusable data products, including Patient 360, Provider 360, and Care Continuum. Ingestion of core clinical source systems (Epic Clarity, FHIR, and HL7) using documented, repeatable patterns. A Data Product Operating Model that turns ad hoc data requests into a prioritized, repeatable intake and delivery process. A research and data science workstream that supports governed data access and production ML use cases. Data governance controls that reduce compliance risk while widening appropriate access to data. What You Will Do: Design, build, and maintain data products on the Lakehouse, from source ingestion through curated, consumption-ready layers. Develop and optimize batch and incremental pipelines in PySpark and SQL, using Delta Lake and medallion-style architecture. Model data for reuse, including entity-centric views such as patient, provider, and care episode. Implement and monitor data quality checks, lineage, and metadata so that each product has a clear owner, definition, and service level. Contribute to the data product catalog and to intake, prioritization, and release practices under the operating model. Partner with data architects, analysts, data scientists, and business stakeholders to translate requirements into well-scoped data product backlog items. Apply data governance and privacy controls for sensitive health data, including access management and de-identification patterns where required. Support downstream consumers such as dashboards, executive applications, and ML models by keeping products reliable and well documented. Review code, write tests, and follow CI/CD practices to keep deployments consistent and safe. Document ingestion and transformation patterns so future data sources can be onboarded faster. Required Qualifications: 5+ years of hands-on data engineering experience building production pipelines and data products. Strong SQL and Python (PySpark), with experience on Databricks or a comparable Spark-based Lakehouse. Working knowledge of dimensional and entity-centric data modeling. Experience with cloud data platforms (AWS, Azure, or GCP) and version-controlled, CI/CD-driven delivery. Experience implementing data quality, testing, and monitoring in production. Clear written and verbal communication, and comfort working directly with key stakeholders. Preferred Qualifications: Healthcare or life sciences data experience, especially Epic Clarity, FHIR, or HL7. Experience with PHI and HIPAA-regulated environments. Familiarity with Unity Catalog, data catalogs, or data governance tooling. Experience with a data product or data mesh operating model. Exposure to ML pipelines, feature engineering, or anomaly detection use cases. Databricks certification (e.g., Data Engineer Associate or Professional). Experience working in agile teams. Tools and Technology Environment Databricks, Delta Lake, Spark (PySpark), SQL, Python, cloud object storage, Git-based CI/CD, and data catalog and lineage tooling.