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Data Quality Analyst - Senior 1

Cummins Talent Acquisition
Pune, IND
Full-time
Senior
Hybrid
Discovered 3 weeks ago
SnowflakeDatabricksSQLMatilliondbtPower BI
Free

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Core Responsibilities Unique to the Role

  • 1) Lead the design, implementation, and continuous evolution of the Enterprise Data Quality Framework across the data ecosystem(ingestion pipelines, data warehouses, semantic layers, and Data Products). Establish scalable standards, policies, controls, and best practices that ensure enterprise data is trusted, governed, certified, and AI-ready. 2) Establish and operationalize data quality capabilities by implementing automated data profiling, validation, reconciliation, monitoring, observability, issue management, and scorecard processes. Define measurable data quality KPIs, SLAs, and governance mechanisms that improve the accuracy, completeness, consistency, timeliness, validity, and reliability of critical enterprise data assets. 3) Partner with Data Engineering, Solution Engineering, Enterprise Data Management (EDM), Architecture, Product Management, cross functional teams and Business teams to embed Data-as-a-Product quality practices throughout the data lifecycle, including business rule management, metadata standards, lineage validation, certification processes, root cause analysis, and proactive remediation of data quality issues.

Required Skills, Education, or Experience

  • 1) Strong hands-on experience designing, implementing, and leading enterprise Data Quality programs and frameworks, including data profiling, validation, reconciliation, root cause analysis, monitoring, issue management, scorecards, and continuous improvement initiatives across large-scale Data Warehouse, Lakehouse, and Data Product environments. 2) Strong expertise in Data Quality, Data Governance, Metadata Management, Data Lineage, Data Stewardship, Master Data Management (MDM), Business Glossary Management, and Data-as-a-Product practices, with experience establishing quality standards, controls, certification processes, and governance frameworks for enterprise data assets. 3) Proven experience implementing and automating data quality controls across the end-to-end data lifecycle, including Source Systems, Data Ingestion, Raw/Bronze, Curated/Silver, Business/Gold, Semantic Models, and Enterprise Data Products, utilizing capabilities such as data profiling, rule-based validations, reconciliations, anomaly detection, exception management, data observability, and quality KPI monitoring. 4) Hands-on expertise with modern cloud data platforms and data engineering technologies, such as Snowflake, Databricks, SQL, Matillion, dbt, and Power BI, with the ability to design scalable data quality solutions and embed quality controls within enterprise data pipelines. 5) Experience with enterprise Data Quality and Governance platforms, and data observability solutions to implement data quality frameworks, metadata management, lineage, cataloging, certification, and governance capabilities across enterprise data products.

Preferred (Nice to Have) Skills, Education, or Experience

  • 1) Experience implementing data quality frameworks for Supply Chain, Quality, Finance, Product Lifecycle, and other Enterprise Products domains within large-scale Data Modernization or Data-as-a-Product initiatives.
  • 2) Experience applying AI/ML and Data Observability capabilities to Data Quality, including anomaly detection, predictive quality monitoring, automated root cause analysis, AI-ready data certification, and governance controls supporting Analytics, Machine Learning, and GenAI solutions. Education Bachelor's degree in Computer Science, Information Technology, Data Analytics, Engineering, Information Systems, or related field. Master's degree preferred.

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