Senior Technical Product Owner - Data Platform

Speridian Technologies•Sacramento, CA

16h•Remote

About The Position

The Senior Technical Product Owner owns the architecture, build-out, governance, privacy enforcement, contract management, and ongoing evolution of DHCS ’s flagship self-service data platform on Databricks Lakehouse, fully implemented in a data mesh architecture tailored to the healthcare domain—focusing on behavioral health (BH) data products, SUD records, and compliance with HIPAA , 42 CFR Part 2 (aligned for treatment/payment/healthcare operations), state/federal BH reporting, and privacy regulations. This deeply technical role owns the central platform engineering backlog and drives rigorous alignment across domain teams, data stewards, policy/compliance experts, and business priorities to enable domain autonomy, treat data as interoperable products, provide frictionless self-serve infrastructure, and enforce federated computational governance via Unity Catalog integrated with Collibra for enterprise cataloging, lineage, quality, stewardship, and policy enforcement. Core technical pillars include: Data contracts REST APIs & API contracts Semantic view layer PHI/ PII privacy protections Data governance & stewardship This role translates strategic vision, OKRs, strict regulatory needs (e.g., 42 CFR Part 2/HIPAA alignment for SUD records), domain/steward inputs, and healthcare interoperability requirements into precise technical backlogs that deliver platform excellence: high adoption, semantic/contract reliability, API usability, PHI/PII protection, governance maturity, performance, scalability, and resilience in a regulated healthcare environment.

Requirements

Advanced backlog/roadmap management for self-service platforms in healthcare data mesh/lakehouse with privacy/governance/semantic/contract/API focus.
Deep fluency in data engineering: ETL/ ELT , medallion, entity resolution, semantic layers, streaming, data contracts (YAML/ODCS), API contracts (OpenAPI), observability.
Mastery of Databricks lakehouse (performance/governance/privacy/contracts) + Collibra (catalog/quality/stewardship/Protect) + PHI/PII patterns + REST/API patterns in BH contexts.
Comfort prototyping/validating in Python/SQL/Spark, including contract validation, API mocks, and privacy controls.
Data-driven prioritization with OKR /metrics emphasis on contract health, API reliability, privacy/governance maturity, adoption, steward enablement in healthcare.
Exceptional technical communication to align engineers, stewards, domains, executives, regulators/policy teams.
Full operational ownership: backlog, incidents, risk/privacy/contract mitigation, capacity—ensuring production stability/resilience/SLAs/compliance.
Strategic thinking: balance delivery with long-term vision (scalability, AI readiness for BH insights, contract/API/governance maturity).
Leadership in highly regulated, cross-functional healthcare settings with tough trade-offs (privacy vs. accessibility, contract rigor vs. usability).

Nice To Haves

Hands-on with Unity Catalog advanced features (Data Classification AI, ABAC, Metric Views composability) + Collibra Protect + data contract tools (e.g., ODCS validation).
Experience defining federated contract/API/stewardship models in healthcare data mesh (domain contracts + central PHI/PII/SUD policies, versioning, Delta Sharing APIs).
Strong experience in entity resolution and Master Data Management (MDM) — including probabilistic/fuzzy matching algorithms, golden record creation, duplicate detection/merging, and ML-driven resolution patterns (e.g., for patient/provider/entity unification across disparate BH/SUD sources like EMRs, claims, counties/MCPs) to enhance data quality, trustworthiness, and interoperability in regulated healthcare environments.
Proven experience delivering AI/ML capabilities for data scientists — including self-serve ML workflows on Databricks (e.g., Mosaic AI for model training/fine-tuning/serving/evaluation/agent frameworks/RAG/GenAI, MLflow for experiment tracking/model registry, feature stores, inference tables, Unity Catalog governance for AI assets/models, secure model serving endpoints) to enable governed, scalable AI/ML in a data mesh (e.g., domain-owned predictive models for BH risk/outcomes, anomaly detection, GenAI-assisted insights) while maintaining PHI/PII compliance and federated access.
Knowledge of BH/SUD privacy/security (de-identification, consent, differential privacy) + enterprise integrations/API gateways.