Data Quality Expert

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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.