Data Quality Expert Job Riyadh area,Riyadh Region Saudi Arabia,IT/Tech

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.

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