Data Quality Expert

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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