Job Description:
• Develop and lead the implementation of enterprise-level data quality standards, policies, and procedures.
• Oversee data quality assessments, audits, and profiling to identify issues, root causes, and improvement opportunities.
• Collaborate with data stewards, data architects, and business stakeholders to establish data quality metrics and KPIs.
• Design and implement robust data cleansing, enrichment, and validation strategies across diverse data sources and platforms.
• Monitor and report on data quality performance, ensuring transparency and accountability throughout the organization.
• Evaluate and integrate data quality tools, technologies, and automation solutions to enhance efficiency and accuracy.
• Provide expert guidance and training to teams on data quality best practices, methodologies, and regulatory compliance.
• Lead cross-functional initiatives to address complex data quality challenges and drive continuous improvement.
• Ensure alignment of data quality efforts with overall data governance and business objectives.
• Stay current with industry trends, emerging technologies, and evolving regulatory requirements to maintain excellence in data quality practices.
Skills
• Data Quality Frameworks and Standards: Deep understanding of frameworks for data quality assessment, monitoring, and improvement.
• Data Profiling and Assessment Tools: Proficiency with data profiling, auditing, and lineage tracking tools (e.g., Collibra, Informatica Data Quality).
• Root Cause Analysis: Ability to identify, analyze, and resolve underlying causes of data quality issues.
• Data Cleansing and Enrichment: Expertise in designing and implementing data cleansing, standardization, and enrichment processes.
• SQL and Scripting Languages: Strong command of SQL and possibly Python or R to query, analyze, and transform data.
• Data Governance and Compliance: Familiarity with governance frameworks, regulatory standards (e.g., GDPR, CCPA), and compliance best practices.
• ETL/ELT Processes: Experience with ETL/ELT tools and techniques to integrate and maintain data quality in data pipelines.
• Performance Measurement: Skill in defining and tracking data quality metrics, KPIs, and performance dashboards.
• Communication and Collaboration: Excellent communication and interpersonal skills to work effectively with stakeholders, data stewards, and technical teams.
• Continuous Improvement Mindset: Commitment to staying updated with industry trends, best practices, and emerging technologies to continually enhance data quality initiatives.
