Solution Architect/Data Governance Architect

IT Applications Solution Architect Senior/Data Governance Architect

Location : Washington, DC (Hybrid)

Duration : Long term

Key Responsibilities

Architect Governance Across Domains

Design and implement governance strategies spanning data (e.g. data access, quality, lineage), platforms (e.g. pipelines, observability, service ownership), and AI (e.g. model transparency, bias detection, monitoring).

Build Tooling and Automation

Define and implement tooling for policy enforcement, access management, metadata tracking, AI explainability, and auditability. Tools may include Collibra, Informatica, Databricks Unity Catalog, custom APIs, or cloud-native policy engines.

Create Reusable Patterns and Templates

Develop modular governance blueprints reference architectures, reusable workflows, templates, and policy-as-code to accelerate delivery while ensuring compliance.

Collaborate Across Product and Architecture

Partner with platform architects, domain leads, data product owners, and legal/compliance to align governance and ethical principles and with business and regulatory needs.

Enable and Educate Teams

Work with enablement and platform teams to embed governance tools into developer workflows, empower stewards and analysts with self-service controls, and drive adoption of best practices.

Monitor and Adapt

Establish KPIs and metrics to measure effectiveness of governance processes and tooling, and iterate based on platform maturity, business feedback, and risk profiles.

Stay current with industry trends, emerging technologies, and best practices. Experiment with new tools and approaches to continuously improve the data Management ecosystem.

What You ll Bring

12+ years of experience in data architecture, enterprise governance, platform engineering, or compliance technology

Deep understanding of modern data stacks (e.g., Databricks, Snowflake, Informatica, Denodo) and governance platforms (e.g., Collibra, Alation, Unity Catalog)

Familiarity with AI governance, including model lifecycle management, transparency, ethics, bias detection, and regulatory frameworks (e.g., NIST RMF, EU AI Act)

Experience with metadata management, access control, lineage, quality frameworks, and enforcement techniques (policy-as-code, APIs, RBAC/ABAC)

Strong systems thinking, with the ability to connect governance strategy to practical engineering constraints

Effective communicator across business, engineering, and compliance audiences