Data Quality Expert

Lenovo; Petaling Jaya; On-site; MYR 180,000 - 280,000; Full time

Job summary

Lenovo is seeking a Senior Data Quality Lead to design and maintain data quality rules, scorecards, and monitoring across enterprise data assets. You will develop automated validation and reconciliation within ingestion to consumption layers and integrate quality checks into CI/CD pipelines.

You will monitor data quality health, investigate incidents, and collaborate with data governance teams to drive remediation. Expertise in SQL, Python, Spark, and cloud data platforms is essential.

Qualifications

• Bachelor’s degree or above in CS/IS/Data Engineering/Statistics or related field.
• 8+ years of experience in Data Quality, Data Governance, Analytics Engineering, or related areas.
• Hands-on experience implementing enterprise data quality solutions and monitoring frameworks.

Responsibilities

• 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.
• 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.
• 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.

Skills

SQL

Python

Spark

Data quality

Education

Bachelor's degree or above in CS/IS/Data Eng/Statistics

Tools

Databricks

Azure Data Factory

Azure Data Lake

Data catalog tools

Job description

• 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.