We are seeking a Data Quality Engineer to design, build, and tune the data quality rules and checks that power our enterprise data quality program. Working in DQLabs (Prizm), SQL Server, and Databricks, this role turns business requirements, gathered by our Data Quality Business Analyst, into reliable, well-performing automated rules and monitoring checks across our insurance data.

Our data quality program operates on a decentralized model: alerts generated by the platform route directly to Business Data Stewards and data owners, who monitor and triage issues within their domains. Because stewards depend on these alerts, this role is accountable not just for building rules, but for their ongoing quality and performance; keeping false positives low, tuning thresholds, and retiring or reworking rules that create noise. Key Responsibilities

Design, develop, and deploy data quality rules and checks in DQLabs against SQL Server and Databricks data sources, based on requirements defined with the Data Quality Business Analyst.
Leverage DQLabs' automated profiling to understand data patterns and quality baselines, using the results to inform rule design and threshold setting.
Spot-check rule results directly against source data (SQL Server, Databricks) when needed to verify flagged records, investigate false positives, and validate thresholds before rules go live.




Own the ongoing performance of deployed rules: monitor false-positive and false-negative rates, tune thresholds and logic, and retire or redesign rules that generate noise or alert fatigue for Business Data Stewards.
Configure alert definitions, severities, and routing metadata so that issues reach the correct Business Data Stewards and data owners with the context they need to triage effectively.
Collaborate with the Data Quality Business Analyst to clarify rule requirements, propose technically feasible approaches, and estimate implementation effort.
Collaborate with the Data Quality Platform Engineer on data source connectivity, platform capabilities, and performance considerations for rule execution.
Investigate root causes of recurring data quality issues surfaced by rules, and work with source system owners and data engineering teams on durable fixes rather than repeated symptom alerts.
Maintain explicit documentation for every rule: business purpose, logic, thresholds, owning steward, and change history.
Follow version control and change management practices for rule development, testing, and promotion.
Contribute to data quality standards, reusable rule templates, and best practices as the program matures.

📌 109619-1- Data Quality Engineer (India)
🏢 Objectwin Technology India
📍 India