Data Governance Engineer

Position Summary

The Data Governance Engineer configures and maintains enterprise data and AI governance capabilities supporting USPS OIG's Responsible AI Framework. The role improves the accuracy, ownership and auditability of governance records and develops usable workflows and integrations for approved business processes. Responsibilities require hands-on Collibra experience, metadata management, workflow development and technical integration skills, with attention to privacy, classification and information handling requirements. This position is contingent based on award, with an anticipated salary range of $145,000-$180,000 based on candidate qualifications and final contract requirements.

Work Environment

• Work Location: Arlington, VA and field offices; remote as Government-approved
• Security Clearance: Government suitability and access requirements to be confirmed

Compensation

Karthik Consulting is committed to providing competitive compensation based on the responsibilities of the role, required qualifications, security clearance level, relevant experience, certifications, customer requirements, geographic location, and overall business needs. The salary range listed for this position represents a good-faith estimate of the current compensation range for this role.

At Karthik Consulting, it is not typical for an individual to be hired at or near the top of the salary range. Final compensation decisions are based on the facts and circumstances of each candidate's experience, qualifications, certifications, clearance level, contract requirements, and overall alignment with the role.

Employees may be eligible for company benefits and additional compensation programs, subject to company policy, contract requirements, and individual eligibility.

Program Context

Program Description

The USPS Office of Inspector General is strengthening its Responsible AI Framework to support the trusted use of artificial intelligence in audits, reviews, and investigations. The program builds on existing AI governance bodies and enterprise technology to advance ethical AI practices, workforce readiness, risk management, use-case evaluation, performance measurement, and data governance. Government personnel retain policy approval, risk acceptance, and decision-making authority.

Program Scope

The program encompasses enhancements to AI ethics controls, workforce guidance and training resources, AI risk registries and validation procedures, use-case intake and review workflows, performance monitoring and ROI measurement, and AI inventory and governance policies. Work includes configuration, integration, documentation, and reporting within the Government's existing enterprise environment.

Key Responsibilities

Governance Platform Configuration

• Configure Collibra assets, attributes, relationships, responsibilities and permissions.
• Translate governance requirements into practical metadata models and platform configurations.
• Apply consistent naming, ownership and lifecycle management practices for enterprise records.
• Assess existing configurations and recommend improvements in usability and maintainability.

Workflow and Integration Engineering

• Develop and maintain forms, review workflows, routing rules and decision records.
• Use APIs, SQL and approved integration tools to exchange information between enterprise services.
• Test workflow behavior, permissions and data exchanges against defined requirements.
• Troubleshoot configuration and integration issues and document corrective actions.

Data Quality and Accountability

• Maintain accurate inventories and governance records for data and AI assets.
• Identify incomplete metadata, inconsistent relationships and gaps in ownership or supporting documentation.
• Apply data quality and lineage concepts to improve trust in governance information.
• Maintain risk information and supporting records in accordance with approved procedures.

Policy Implementation and Support

• Translate classification, privacy, access and information handling requirements into appropriate platform controls.
• Prepare documentation and supporting records for governance, audit and compliance reviews.
• Clarify governance requirements with technical and business stakeholders and validate that forms, records and workflows are usable.
• Develop administration procedures and provide knowledge transfer to designated platform owners.

Expected Outcomes

• AI and data governance records with consistent identifiers, assigned ownership, lifecycle information and traceable supporting documentation.
• Tested forms, routing and review workflows that capture required information and maintain usable decision records.
• Platform configurations that apply approved metadata, access and information handling requirements and support governance reviews.
• Documented integration behavior, administration procedures and issue resolutions that enable reliable platform maintenance.

Qualifications

Education

Bachelor's degree in information systems, computer science, data management or engineering, or equivalent relevant experience. A relevant master's degree is preferred.

Professional Certifications

Preferred: Relevant certification in Collibra, data governance or data management.

Minimum Qualifications

• At least 7 years of relevant data governance, metadata, data engineering or workflow implementation experience.
• Hands-on Collibra experience configuring assets, attributes, responsibilities, permissions and workflows.
• Experience developing intake forms, review routing and decision records in an enterprise environment.
• Ability to integrate platforms through APIs, SQL or comparable interfaces and troubleshoot inconsistencies.
• Experience translating classification, privacy and handling requirements into metadata and controls.
• Ability to document procedures and maintain evidence for audit and compliance reviews.

Preferred Qualifications

• Experience with Databricks Unity Catalog and catalog-to-model governance connections.
• Microsoft 365 workflow tools and Jira/Confluence integration.
• AI inventory, model metadata or risk registry implementation.
• Familiarity with data lineage, quality controls and lifecycle management.