Data Engineering Manager, Master Data

Job Title: Data Engineering Manager, Master Data – Behavioral Health Transformation
Location: Remote (Working hours are Monday-Friday PST)
Division: Medical Enterprise System Modernization Division (MESMD)
Department: California Department of Health Care Services (DHCS)
Must work on shore in the US. We cannot sponsor or transfer an H1B Visa for this role.
Ready to democratize data for California's healthcare revolution? Join the Department of Healthcare Services (DHCS) as a Data Engineering Manager, where you'll architect the data platforms that power evidence-based decisions affecting 14 million Californians' healthcare journeys.
The Data Engineering Manager, Master Data, leads the engineering team responsible for designing, building, deploying, and operating enterprise data pipelines, master data capabilities, and reusable data products that support healthcare programs, operations, quality measurement, and interoperability.
Working closely with the Director, Data Engineering and the Director, Data Portfolio, this role manages engineering execution across Data Ingestion and Transformation, Entity Resolution, Master Data Management, Reference Data, Consent-Informed Data Sharing, Data Products, and the enterprise Data Platform.
The Manager is accountable for day-to-day engineering execution, technical implementation quality, team performance, delivery predictability, and production reliability. This includes managing engineering capacity, work estimates, development activities, testing, deployment, and operational support across a modern lakehouse environment.
This is a player-coach role. The Manager writes, tests, and ships production code as a working member of the team and spends about half of their time on hands-on engineering. The Manager also leads through design reviews, code reviews, troubleshooting, and engineering standards. The Manager works within established enterprise architecture direction and partners with architecture, product, interoperability, privacy, and data governance teams to translate approved business requirements and technical designs into scalable, secure, and maintainable production solutions.
We value leaders who do the work. The most effective leaders on our teams write code, review code, and fix production issues with their engineers.
Key Responsibilities
Engineering Leadership and Delivery
Write, test, and ship production code for pipelines, data products, and master data services, and take assigned stories in each sprint.
Join the on-call rotation and work production incidents directly, through root-cause analysis and the fix.
Lead, mentor, and develop a team of data engineers, establishing clear expectations for technical quality, individual accountability, performance, and delivery.
Manage day-to-day engineering execution, including estimates, sprint planning, resource allocation, team capacity, technical dependencies, and delivery commitments.
Provide technical leadership through design reviews, code reviews, implementation guidance, and troubleshooting of complex data engineering challenges.
Establish and enforce engineering standards for code quality, documentation, performance, scalability, maintainability, and operational readiness.
Translate approved architecture designs and business requirements into executable engineering plans, identifying technical tradeoffs, dependencies, and implementation risks early.
Partner with the Director, Data Engineering and the Director, Data Portfolio to align engineering capacity, sequencing, and execution commitments with portfolio priorities.
Identify and resolve engineering risks, delivery constraints, and technical blockers, escalating decisions and dependencies when appropriate.
Data Platform, Pipelines, and Data Products
Build, test, deploy, and operate enterprise data ingestion, integration, transformation, and orchestration pipelines on a modern lakehouse platform, and lead the team doing the same work.
Implement layered data architectures, including bronze, silver, and gold data processing patterns, in accordance with established enterprise architecture standards.
Engineer and deliver reusable, governed data products, including curated datasets, analytical data models, and published data assets that support healthcare operations, quality measurement, reporting, and interoperability.
Implement quality controls, lineage, metadata, and operational monitoring to ensure data products are trusted, traceable, and fit for their intended use.
Optimize distributed data processing workloads for performance, scalability, reliability, and cost efficiency.
Master Data Management and Enterprise Data Services
Build enterprise Master Data Management capabilities and support them in production, including Entity Resolution, master record management, data standardization, matching, consolidation, survivorship, and publication of authoritative master data.
Build and maintain engineering solutions supporting deterministic and probabilistic entity matching, identity reconciliation, and exception handling in accordance with approved requirements and technical designs.
Implement Reference Data capabilities, including controlled vocabularies, terminology mappings, reference data pipelines, and integration with enterprise data services.
Develop and maintain integrations that make mastered data and reference data consistently available to enterprise applications, analytical platforms, and downstream data products.
Partner with architecture, data governance, and business stakeholders to implement approved matching rules, mastering requirements, data quality controls, and stewardship workflows.
Data Quality, Security, and Production Operations
Establish and enforce automated testing, data validation, reconciliation, regression testing, and deployment verification practices across engineering solutions.
Embed data quality checks, metadata capture, lineage, auditability, and traceability into pipelines, master data services, and data products.
Implement consent-informed data access and sharing controls for protected health information in accordance with approved privacy policies, consent requirements, and enterprise security standards.
Ensure engineering solutions comply with established CI/CD processes, security requirements, change management procedures, and deployment controls.
Maintain production reliability through monitoring, observability, incident response, failure recovery, root-cause analysis, and continuous operational improvement.
Establish and monitor engineering performance indicators covering delivery predictability, pipeline reliability, data processing performance, defect rates, and operational effectiveness.
Required Qualifications
7+ years of progressive data engineering experience, including at least 3 years leading engineering teams while writing production code.
Has written and shipped production Spark, Python, and SQL code in the last 12 months on Databricks or a comparable lakehouse platform. Can walk through a pipeline or data product they built: the design, the code, and how it ran in production.
Demonstrated ability to lead technical design reviews, evaluate engineering implementations, troubleshoot complex processing issues, and establish effective engineering practices.
Experience implementing layered (medallion) data architectures, ETL/ELT pipelines, distributed data processing, orchestration, and analytical data modeling.
Experience developing reusable data products, including curated datasets, data quality controls, metadata, lineage, and integration with downstream consumers.
Experience implementing or supporting enterprise Master Data Management capabilities, including entity resolution, record matching, master data integration, standardization, or survivorship.
Experience working with Medicaid, health plan, or payer data, including claims, encounters, eligibility, and member information.
Experience integrating healthcare data across enterprise systems using batch processing, APIs, and other data exchange mechanisms.
Experience managing engineering teams through agile delivery practices, including work estimation, sprint execution, capacity planning, and technical dependency management.
Experience implementing automated testing, data validation, reconciliation, CI/CD, and production monitoring for enterprise data solutions.
Working knowledge of HIPAA, 42 CFR Part 2, and technical controls for protecting sensitive healthcare information.
Experience delivering engineering solutions in multi-vendor environments with formal architecture, security, change management, and deployment controls.
Strong communication, problem-solving, and stakeholder management skills, with the ability to engage technical and nontechnical audiences.
Preferred Qualifications
Experience supporting a state Medicaid agency, public-sector healthcare program, or large-scale healthcare modernization initiative.
Experience implementing Entity Resolution solutions using deterministic or probabilistic matching, including match scoring, precision and recall evaluation, and identity reconciliation workflows.
Experience with enterprise MDM platforms, master patient indexes, golden record management, crosswalks, and master data publication services.
Experience with Databricks Delta Lake, Uni

Work arrangement
Yes