The Solution Architect, Data Governance is a senior technical leadership role responsible for defining, evolving, and governing the end-to-end architecture of client enterprise Data Governance ecosystem. This architect will own the technical vision and integration strategy across a broad and growing platform portfolio — including Open Metadata, Collibra, Ataccama, Securiti.ai, alignment with Fabric Semantic Layer, and a proprietary Metric Catalog — ensuring these platforms operate as a cohesive, scalable, and future-ready governance fabric across Client's data estate.
This is a strategic and hands-on role that operates at the intersection of enterprise architecture, data platform engineering, and governance program leadership. The successful candidate will shape how Client governs data at enterprise scale: designing integration patterns, defining platform standards, guiding AI-assisted governance capabilities, and serving as the architectural authority for governance decisions across engineering, product, and business teams.
What You'll Do
Governance Platform Architecture & Strategy
• Own the end-to-end technical architecture for Client's Data Governance platform ecosystem, spanning Open Metadata, Collibra, Ataccama, Securiti.ai, the Semantic Layer, and the Metric Catalog.
• Define and maintain the architectural roadmap for each platform — including integration patterns, data flows, API strategies, scalability plans, and platform evolution — aligned to the broader Data & Intelligence strategy.
• Design the integration architecture connecting governance platforms to Client's data infrastructure: Databricks, Unity Catalog, Azure, Microsoft Fabric, DBT, and Power BI.
• Establish and enforce governance platform standards, reference architectures, and technical guardrails that engineering teams follow when building on or extending these platforms.
• Evaluate new technologies, vendors, and open-source frameworks; produce architecture decision records (ADRs) and recommendations for platform evolution.
Open Metadata, Semantic Layer & Metric Catalog Architecture
• Serve as the architect for Client's Open Metadata deployment — defining ingestion strategies, connector architecture, metadata models, lineage capture patterns, and API integration approaches.
• Architect governance tools and solutions to support the Semantic Layer to serve as a single source of truth for business metrics and KPIs, defining how physical data assets are mapped to semantic definitions consumed by BI tools, AI agents, and downstream applications.
• Design the Metric Catalog architecture — governing how business metrics are defined, versioned, certified, and published across Client's analytics and reporting ecosystem.
• Ensure the three platforms (Open Metadata, Semantic Layer, Metric Catalog) are architecturally coherent and interoperable: certified metrics are registered and discoverable through Open Metadata and the Catalog.
Enterprise Integration & Data Platform Architecture
• Design scalable metadata ingestion and propagation patterns across Client's 4,000+ applications, ensuring governance platforms receive timely, accurate, and complete metadata at scale.
• Architect event-driven and API-based integration patterns between governance platforms and source systems, data pipelines, and consuming applications.
• Define data lineage architecture end-to-end — from source system through transformation layers (DBT, ADF, PySpark) to consumption — ensuring lineage is captured, stored, and surfaced consistently across the governance ecosystem.
• Partner with Data Engineering and Data Architecture teams to embed governance into platform design — ensuring that new data products, pipelines, and domains are governance-ready from inception.
AI-Assisted Governance Architecture
• Define the architectural patterns for AI-assisted governance capabilities — including automated data classification, intelligent metadata enrichment, lineage inference, natural language data discovery, and AI-driven data quality — ensuring they integrate cleanly with the governance platform ecosystem.
• Collaborate with AI Engineering teams to design the interfaces between AI agents and governance platforms: how models consume metadata, how outputs are validated and written back, and how human-in-the-loop controls are enforced.
• Architect responsible AI guardrails within the governance platform ecosystem, including audit logging, observability, and explainability requirements for AI-assisted decisions.
Compliance & Security Architecture
• Ensure the governance platform architecture supports Client's regulatory obligations including CCPA/CPRA, USGCI, FCC requirements, ISO 42001, ISO 27001, and Client's internal TISS-310 data classification standards.
• Architect data access controls, sensitive data handling, and privacy-by-design patterns within governance platforms — including how Securiti.ai integrates with classification, consent, and rights fulfillment workflows.
• Collaborate with Cybersecurity and Privacy Operations teams to ensure governance architecture decisions meet current and emerging compliance requirements.
Technical Leadership & Stakeholder Engagement
• Serve as the architectural authority and technical escalation point for Data Governance engineering teams — providing guidance on complex integration challenges, platform decisions, and design tradeoffs.
• Facilitate architecture reviews, design sessions, and technical working groups with engineering, data, product, and business stakeholders.
• Produce and maintain comprehensive architecture documentation: platform architecture diagrams, integration maps, data flow diagrams, ADRs, and governance platform standards.
• Mentor senior engineers on architecture principles, integration patterns, and platform engineering best practices.
• Represent the Data Governance architecture perspective in enterprise architecture forums, data council meetings, and cross-functional governance bodies.
What You'll Bring
Education & Experience
• Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or a related technical field.
• 10+ years of hands-on experience in data engineering, data architecture, or solution architecture in large-scale enterprise environments.
• Proven track record designing and implementing enterprise-scale data governance or data platform architectures across multiple platforms and organizational domains.
Architecture & Platform Expertise
• Deep expertise with enterprise Data Governance platforms — including Open Metadata, Collibra, Ataccama, or Securiti.ai — with the ability to architect multi-platform integrations, not just administer individual tools.
• Strong hands-on experience designing Semantic Layer architectures (dbt Semantic Layer, AtScale, Cube.dev, or equivalent) and Metric Catalog solutions.
• Broad expertise across T-Client's data platform stack or equivalent: Databricks, Unity Catalog, Azure (ADF, ADLS, Synapse), Microsoft Fabric, DBT, and Power BI.
• Experience designing event-driven and API-first integration architectures using Kafka, REST APIs, GraphQL, or equivalent patterns.
• Strong working knowledge of data modeling, metadata standards (OpenLineage, OpenAPI, DCAT), and enterprise data architecture patterns.

Sr. Solution Architect with Data Governance

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