Data Quality Expert

Key Responsibilities

Data Quality Engineering

• Design, develop, and maintain scalable data quality rules, scorecards, and monitoring mechanisms across enterprise data assets.
• Develop automated data validation and reconciliation processes throughout data ingestion, transformation, and consumption layers.
• Design proper solution to integrate data quality checks into data pipelines and CI/CD deployment processes to enable automated quality enforcement.

Monitoring & Operational Excellence

• Monitor enterprise data quality health and proactively identify quality issues, anomalies, and risks.
• Investigate root causes of data quality incidents and collaborate with engineering and business teams to drive remediation.
• Establish alerts, dashboards, KPI tracking, and reporting mechanisms for enterprise data quality performance.
• Support operational governance processes for issue management, escalation, tracking, and resolution.

Platform & Automation Enablement

• Partner with data platform teams to embed data quality capabilities into enterprise data platforms.
• Contribute to self-service data quality capabilities, reusable rule libraries, and standardized quality frameworks.
• Leverage AI and automation technologies to improve rule generation, anomaly detection, root cause analysis, and monitoring efficiency.
• Support continuous enhancement of Data Quality Services (DQS), scorecard engines, and related platforms.

Governance & Stakeholder Collaboration

• Collaborate with Data Governance, Data Stewards, Data Owners, and business teams to define quality requirements and acceptance criteria.
• Translate business requirements into executable technical data quality controls.
• Support enterprise standards, policies, and governance processes related to data quality.
• Drive adoption of data quality best practices across business and IT organizations.

Qualifications

Education

• Bachelor's degree or above in Computer Science, Information Systems, Data Engineering, Statistics, or a related discipline.

Experience

• 8+ years of experience in Data Quality, Data Engineering, Data Governance, Analytics Engineering, or related fields.
• Hands-on experience implementing enterprise data quality solutions and monitoring frameworks.
• Experience working with large-scale data platforms, data warehouses, data lakes, or lakehouse architectures.

Technical Skills

• Strong SQL skills with experience in data profiling, validation, reconciliation, and troubleshooting.
• Proficiency in Python, Spark, PySpark, or similar data engineering technologies.
• Experience with Databricks, Azure Data Factory, Azure Data Lake, or comparable cloud-based data platforms.
• Familiarity with data quality tools, metadata management, catalog solutions, etc.
• Knowledge of data modeling, ETL/ELT processes, and data pipeline orchestration.

Preferred Qualifications

• Understanding of Data Governance, Metadata Management, Master Data Management (MDM), and Data Lineage concepts.
• Experience implementing rule-based and AI-driven data quality capabilities.
• Knowledge of enterprise data architecture and analytics ecosystems.
• Experience supporting global or cross-functional data programs.
• Relevant certifications in Azure, Databricks, Data Management, or Data Governance are a plus.