Data Integrity Analyst

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.