Data Quality Analyst

Seize your opportunity to make a personal impact supporting the Case Management Modernization (CMM) Program. The CMM program is an initiative to support the Administrative Office of the US Courts (AO) in developing a modern cloud-based solution to support all 204+ federal courts across the United States.

GDIT is your place to make meaningful contributions to challenging projects and grow a rewarding career. The Data Quality Analyst will be part of the CMM Data Modernization and Governance team responsible for delivering an integrated data governance, engineering, data platform, reporting, analytics, and Artificial Intelligence (AI)/Machine Learning (ML) capabilities that support operational decision-making and fulfill AO's data and analytics objectives in support of the CMM program.

The successful candidate will be responsible for establishing and operationalizing data quality practices across CMM data domains and systems to accelerate CMM modernization efforts. The role defines, implements, monitors, and continuously improves data quality rules, controls, metrics, and processes to ensure data is accurate, complete, consistent, timely, valid, unique, and fit for its intended purpose. The role investigates and resolves data quality issues, and partners with data owners and stewards to drive ongoing improvements to data accuracy, completeness, and reliability, and supports alignment with Judiciary policies, practices, and the current Data Governance Roadmap, and fosters a data driven culture across the AO and local courts.

The Data Quality Analyst will execute the following responsibilities:

• Define, document, and maintain data quality rules, controls, metrics, thresholds, business validation criteria, and measurement frameworks.
• Establish data quality requirements in collaboration with Data Owners, Data Stewards, business stakeholders, and technical teams.
• Assess data quality across critical data domains, systems, interfaces, pipelines, and data products.
• Develop data quality profiles, baselines, scorecards, dashboards, and trend analysis.
• Identify, investigate, document, and prioritize data quality issues.
• Perform root-cause analysis to determine whether issues originate from source systems, business processes, data transformations, integrations, or downstream consumption.
• Establish data quality remediation processes and track issues through resolution.
• Monitor data quality against established thresholds and service-level expectations.
• Implement automated data quality checks and controls where appropriate.
• Define data quality dimensions including accuracy, completeness, consistency, timeliness, validity, uniqueness, and integrity.
• Partner with Data Engineers and application teams to embed quality controls within ETL/ELT pipelines and data products.
• Support data quality requirements for data modernization, migration, integration, and transformation initiatives.
• Establish continuous monitoring and alerting for critical data quality conditions.
• Identify recurring data quality issues and recommend processes, system, or governance improvements.
• Collaborate with Data Governance and Metadata teams to align data quality rules with approved data standards, definitions, and business glossary terms.
• Support development of data quality standards, policies, procedures, and operating practices.
• Provide data quality reporting to governance councils and program leadership.
• Contribute to continuous improvement of the CMM data quality maturity model.

• A minimum of 8+ years’ experience, of which at least 3+ years is specialized experience in areas such as the following: data quality analysis and design of business applications on complex systems for large-scale computers, data base management, use of programming languages, and/or DBMS.
• Demonstrated experience in supporting enterprise data governance programs.
• Experience translating governance policy into practical operational processes in delivery environments.
• Strong understanding of data quality management, metadata practices, and stewardship models.
• Experience governing high-sensitivity or regulated data with clear compliance expectations.
• Experience with data quality profiling and monitoring techniques and common tooling patterns.
• Experience defining and governing enterprise data standards across multiple teams and delivery pods.
• Strong background in data modeling, integration patterns, and change data capture concepts.
• Experience with metadata and catalog tools.
• Practical experience implementing glossary and lineage capabilities in modern data platforms.
• Ability to translate technical metadata into business-friendly definitions and documentation.
• Experience implementing data privacy controls in regulated or high-sensitivity environments.
• Experience with access management and operationalizing privacy requirements.
• Experience partnering with security, governance, and engineering teams to implement compliant data sharing.
  

• Experience in Federal Government or regulated environments.
• Experience with enterprise data governance frameworks and operating models.
• Data management certification.
• Experience with cloud-based data quality platforms, tools, and services (e.g., Collibra, Informatica, Snowflake, Databricks, or AWS).

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Originally posted on Himalayas