Senior Quality Engineer

Primary Responsibilities

• Design, build, and maintain scalable data quality frameworks to validate accuracy, completeness, consistency, and timeliness of enterprise data across multiple data domains
• Implement data validation, reconciliation, and anomaly detection logic across batch and streaming data pipelines
• Embed automated data quality checks into ETL / ELT pipelines to enforce quality gates and prevent defective data from propagating downstream
• Build reusable data quality automation components, libraries, and frameworks that can be consistently adopted across data engineering teams
• Validate Power BI datasets, semantic models, and dashboards by ensuring: Source-to-report data reconciliation, Metric and KPI accuracy, Aggregation, filter, and refresh correctness
• Partner with reporting and analytics teams to validate Power BI measures, calculations, and business logic against certified data sources
• Monitor and report on data quality metrics and trends, including dashboard-level data accuracy and consistency
• Perform root cause analysis for data quality and reporting issues, collaborating with upstream data producers and downstream consumers to drive permanent fixes
• Leverage AI-assisted data quality practices, including: GenAI-based rule generation and test case creation, Intelligent anomaly detection and pattern recognition, Automated triage and summarization of data quality issues
• Enable AIOps-style data observability, using AI-driven insights to proactively identify data drift, schema changes, and metric anomalies
• Support governance and audit readiness by ensuring data quality controls, validations, and dashboard certifications are documented and traceable
• Continuously improve data quality practices through automation, standardization, and AI-driven enhancements, reducing manual validation effort
• Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, or IT related field
• 5+ years of experience in data quality engineering, quality engineering, or data engineering roles with a strong focus on automation and frameworks with a solid understanding of data quality dimensions (accuracy, completeness, consistency, timeliness, validity)
• 5+ years of experience with SQL for data profiling, reconciliation, and validation
• 5+ years of experience implementing automated data quality frameworks embedded within ETL / ELT workflows
• 3+ years of experience in Python and PySpark, including building reusable notebooks and automation frameworks
• 3+ years of proven experience working in a Databricks environment, supporting large-scale data pipelines and analytics platforms
• 2+ years of hands-on experience validating Power BI datasets, dashboards, KPIs, and metrics against source systems

Preferred Qualifications

• Strong communication skills with the ability to translate business reporting requirements into technical quality controls