Job Description: Data Integrity Analyst Primary Role Ensure data accuracy, consistency, and reliability across the organization. Verify and cleanse data to maintain high-quality standards and Ensure adherence to data governance policies and regulatory requirements. Generate data quality reports for stakeholder communication. Assist in implementing and enforcing data governance practices and Maintain clear records of data quality processes and standards.
Work directly with the Data Architecture Manager to execute a wide variety of dynamic, high-priority strategic projects, aimed at generating insights to make our business run more efficiently, and grow our company as fast as possible Work with other teams to address data-related challenges. Duties & Responsibilities Data Quality Assurance:
Perform regular audits on datasets to ensure data quality, completeness, and accuracy.
Develop and implement data quality protocols and standards.
Identify, analyze, and correct data discrepancies and inconsistencies.
Data Governance:
Maintain data integrity by implementing and overseeing data management processes.
Ensure compliance with industry standards, regulations, and best practices related to data management.
Data Monitoring and Reporting:
Generate regular and ad-hoc reports to communicate data quality status to stakeholders.
Monitor data quality metrics and report on data integrity issues.
Root Cause Analysis:
Investigate data quality issues to determine the root causes.
Recommend and implement solutions to prevent recurrence of data issues and document findings and share knowledge across teams to foster a culture of data quality awareness.
Data Security and Privacy:
Ensure that data handling processes comply with data privacy laws and organizational security policies.
Monitor access to data and ensure that sensitive information is adequately protected.
Collaboration:
Work closely with data scientists, data analysts, and business units to understand data needs and issues.
Act as a liaison between technical teams and business units to ensure data requirements are met.
Documentation:
Maintain detailed documentation of data quality procedures, policies, and standards.
Document data lineage and maintain records of data sources and transformation processes.
Continuous Learning:
Stay updated on industry trends and enhance data management processes.
Continuously improve skills and adopt new tools and techniques.
Key Outcomes High Data Quality: Achieving and maintaining high levels of data accuracy, completeness, consistency, and reliability. Reduction in the occurrence of data errors, discrepancies, and inconsistencies.
Enhanced Data Governance: Effective implementation and adherence to data governance policies and procedures. Improved compliance with industry regulations and internal data management standards.
Accurate Data Reporting: Production of reliable and accurate reports, dashboards, and data visualizations.
Proactive Issue Resolution: Early identification and resolution of data quality issues. Implementation of preventive measures to avoid recurring data problems.
Cross-Functional Collaboration:Enhanced understanding and alignment of data quality objectives across the organization.
Feedback Loop for Continuous Improvement: Actively seek feedback from managers to improve the relevance and effectiveness of reports. Iterate on report designs and content based on the evolving needs of the organization.
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