Data Quality Expert

4 days ago

Riyadh, Ar Riyāḑ, Saudi Arabia Giza Systems KSA Full time
  • 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.
Personal 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.
Technical Skills

Data Quality Tools: Proficiency with industry-standard data quality and profiling tools (e.g., Informatica Data Quality, Talend Data Quality, Collibra).

SQL and Database Querying: Advanced SQL skills to query databases, identify anomalies, and perform complex data transformations.

Scripting and Programming: Familiarity with scripting languages (e.g., Python, R) for data cleansing, enrichment, and automation tasks.

ETL/ELT Tools: Experience with ETL/ELT platforms (e.g., Informatica, Talend, AWS Glue) to implement data quality rules and workflows.

Metadata Management: Ability to leverage metadata repositories and data catalogs to maintain data lineage and track data quality improvements.

Big Data and Cloud Platforms: Knowledge of big data ecosystems (Hadoop, Spark) and cloud environments (AWS, Azure, GCP) for scalable data quality solutions.

Data Governance Technologies: Familiarity with governance platforms and frameworks to ensure alignment of data quality initiatives with organizational standards.

Version Control and CI/CD: Experience using Git, Jenkins, or similar tools to manage code, automate deployments, and streamline data quality integrations.

Data Visualization: Skill in creating dashboards and visualizations (e.g., Tableau, Power BI) to communicate data quality metrics and trends.

APIs and Integration Technologies: Ability to use APIs, RESTful services, or messaging systems (e.g., Kafka) for seamless data integration and quality checks.

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