Data Quality Analyst

GDIT is a technology and professional services company delivering technology solutions and mission services to government, defense, and intelligence organizations. The Data Quality Analyst will establish, operationalize, monitor, and continuously improve data quality practices across the Case Management Modernization program, including rules, controls, metrics, and remediation processes. The role partners with data owners, stewards, governance, engineering, and application teams to improve data accuracy, reliability, compliance, and usability.

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

Requirements:

• Analytics
• Data Accuracy
• Data Quality
• 6 + years of related experience
• A minimum of 6+ 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)