We are seeking a Senior Data Architect with deep healthcare payer expertise to lead the design and governance of enterprise data platforms in a complex, regulated environment. The ideal candidate brings proven experience architecting large-scale data solutions across heterogeneous environments — spanning legacy on-premises systems and modern cloud-native platforms — with a strong command of healthcare payer data domains including claims, member enrollment, provider management, and clinical data.
This role requires someone who can translate complex business and regulatory requirements into scalable, compliant data architectures, and who is comfortable engaging with both senior stakeholders and engineering teams to drive delivery.
Primary Responsibilities
Architecture & Design
• Define and govern enterprise data architecture standards across ingestion, storage, transformation, and serving layers, ensuring consistency, scalability, and alignment with business objectives.
• Design and maintain conceptual, logical, and physical data models for healthcare payer data domains — including claims adjudication, member enrollment, provider credentialing, risk adjustment, and drug/pharmacy data.
• Establish data architecture patterns for Lakehouse environments, including open table format standards, layer separation (Raw, Curated, Serving), and clear data contract definitions between layers.
• Design integration architectures for heterogeneous data environments spanning on-premises relational systems (DB2, Oracle), virtual integration layers (Denodo), flat-file and SFTP-based sources, and cloud-native data services on Google Cloud Platform.
• Define standards for real-time and batch data processing patterns, ensuring the architecture supports both operational sub-millisecond response requirements and large-scale analytical workloads on platforms such as BigQuery and Dataproc.
• Evaluate and recommend technologies across the data platform stack — including data catalog, metadata management, data observability, and federated analytics tools — aligned to enterprise roadmap direction.
Governance & Standards
• Establish and enforce data governance standards including metadata management, data lineage, data quality, and master data policies across the enterprise data platform.
• Conduct data model reviews and architecture assessments, identifying risks, gaps, and opportunities for standardization across teams and workstreams.
• Ensure all data architecture decisions comply with HIPAA, NIST, FedRAMP, and CMS regulatory requirements applicable to healthcare payer data.
• Define data security and privacy-by-design principles including column-level encryption, role-based access control, and PHI/PII handling standards across data platform layers.
Collaboration & Leadership
• Partner with engineering leads, business analysts, and domain SMEs across Claims, Member/Enrollment, Provider, Finance, and Clinical workstreams to translate requirements into data designs.
• Lead architecture reviews and working sessions, producing documentation and design artifacts consumable by both technical teams and business stakeholders.
• Mentor data engineers and junior architects, establishing a culture of data quality, design rigor, and platform thinking within the team.
• Stay current with evolving data platform technologies, industry standards (DMBoK), and healthcare data regulations, bringing relevant insights into architectural decisions.
Minimum Qualifications
• Bachelor''s or Master''s degree in Computer Science, Information Systems, Engineering, or a related field.
• 10+ years of progressive data architecture experience, with at least 5 years in a Senior or Lead Data Architect capacity within healthcare payer or similarly regulated industries (Medicaid, Medicare Advantage, commercial insurance).
• Proven expertise designing enterprise data platforms on Google Cloud Platform (Google Cloud Platform), with hands-on experience across BigQuery, Dataproc, Cloud Storage, and Cloud Composer.
• Strong proficiency in data modeling — relational, dimensional, and NoSQL paradigms — using industry-standard tools such as ERwin, Lucidchart, or equivalent.
• Deep understanding of healthcare payer data domains: claims processing, member enrollment, provider networks, risk adjustment, HEDIS, and regulatory reporting.
• Experience designing data architectures for heterogeneous environments including on-premises relational systems (DB2, Oracle), virtual data layers (Denodo), and cloud-native platforms.
• Working knowledge of Lakehouse architecture patterns including open table formats (Apache Iceberg), schema evolution, partitioning strategies, and ACID transaction management.
• Strong understanding of HIPAA compliance requirements and how they translate into data architecture and security design decisions.
Preferred Experience
• Experience with federated analytics patterns and query engines that operate across disparate data sources including cloud data warehouses, operational databases, and Iceberg-based data lakes.
• Familiarity with modern metadata catalog and data observability platforms for enterprise lineage, data quality monitoring, and stewardship — particularly in a roadmap or adoption context.
• Experience designing for sub-millisecond operational data serving requirements, including optimization strategies for high-throughput transactional reads alongside analytical workloads.
• Exposure to streaming architectures using Apache Kafka or Confluent for real-time data ingestion and event-driven processing in healthcare environments.
• Knowledge of Infrastructure-as-Code (IaC) practices and deployment pipeline standards in Google Cloud Platform environments.
• Excellent communication and presentation skills; ability to engage technical teams and executive stakeholders with equal effectiveness.
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