What are the responsibilities and job description for the Data Analyst position at Government Systems Technologies Inc. (GSTi)?
Role: Data Analyst
Location: Alpharetta, GA
Duration: 6 months CTH
Responsibilities
- Data Profiling: Analyze and examine large datasets to understand structure, completeness, accuracy, and relationships. Identify data anomalies, redundancies, and opportunities for standardization and improvement.
- Data Quality Assessment: Monitor, measure, and report on data quality metrics such as completeness, accuracy, consistency, timeliness, and uniqueness across key datasets and systems.
- Data Cataloging (EDO/Data Catalog): Maintain and curate the enterprise data catalog using tools such as Data EDO. Ensure that metadata, lineage, ownership, and business definitions are captured and updated.
- Data Quality Rule Creation: Design, implement, and maintain data quality rules and validation scripts. Collaborate with business and technical teams to translate requirements into effective data quality checks and alerts.
- Data Cleansing & Standardization: Develop and execute data cleansing and transformation routines to remediate quality issues and enforce enterprise standards.
- Stakeholder Collaboration: Work with data stewards, owners, data engineers, and business analysts to resolve data quality issues and enhance understanding of data assets.
- Documentation & Reporting: Prepare comprehensive data quality reports, visualizations, and dashboards to communicate findings and improvement initiatives to stakeholders.
- Continuous Improvement: Recommend and drive improvements in data quality processes, tools, and best practices.
Required Skills
- Good experience in data analysis, data quality, or data governance roles.
- Hands-on experience with data profiling, quality, and cataloging tools (e.g., Data EDO, Collibra, Informatica, Alation, Talend).
- Experience with data visualization/dashboarding tools (Tableau, Power BI, Looker).
- Experience in writing and deploying data quality rules, validations, and remediation scripts.
- Strong analytical, problem-solving, and documentation skills.
- Familiarity with modern data architectures (data lakes, warehouses) is an advantage.
- Advanced SQL skills and proficiency working with large structured and semi-structured datasets.
- Excellent written and verbal communication skills.