DE&A - Core - Data Quality Management - Data Quality Management (Other)

We are seeking a hands on QA Lead to drive quality assurance for a scalable, enterprise-wide data platform for an insurance client. The role involves validating batch and real-time data pipelines, ensuring data accuracy across Raw, Silver, and Gold layers, and supporting report rationalization and self-service analytics (Power BI).
The QA Lead will define and implement end-to-end data testing strategies, covering ingestion (AWS Glue, Kinesis), transformation (DBT), and consumption layers, while ensuring data quality, integrity, and performance optimization.
Key Responsibilities
1. QA Strategy & Leadership
Define and implement end-to-end QA strategy for the enterprise data platform
Establish test frameworks, standards, and governance for data validation
Lead QA planning, estimation, and execution across multiple data streams
2. Data Validation & Testing
Validate data across Raw, Silver, and Gold layers ensuring accuracy, completeness, and consistency
Perform source-to-target reconciliation for batch and real-time pipelines
Design and execute: Data quality checks
Transformation validation (DBT models)
Aggregation and KPI validation

3. Batch & Real-Time Pipeline Testing
Test batch ingestion pipelines using AWS Glue
Validate real-time streaming data pipelines using Amazon Kinesis
Ensure data latency, sequencing, and event consistency in streaming pipelines
4. Reporting & Rationalization QA
Validate datasets powering Power BI self-service reports
Support report rationalization initiatives by ensuring consistency of KPIs and eliminating redundant data sources
Perform report/data reconciliation testing across legacy vs new platform
5. Automation & Tools
Develop and implement automated data testing frameworks
Leverage SQL, Python, and testing tools (e.g., Great Expectations, DBT tests, custom frameworks)
Enable continuous testing integration within CI/CD pipelines
6. Performance & Optimization Testing
Validate performance of: Data pipelines
Queries in Snowflake

Identify bottlenecks and work with engineering teams to optimize pipelines and queries
Ensure scalability for large data volumes and concurrent workloads
7. Data Quality & Governance
Define and enforce data quality rules, thresholds, and monitoring
Implement data anomaly detection and alerting mechanisms
Ensure compliance with audit, reconciliation, and governance standards
Required Skills & Experience
Core Technical Skills
Strong experience in data testing / ETL testing / data QA
Hands-on expertise with: Snowflake (data validation, SQL testing)
DBT (testing, model validation)
AWS Glue (batch pipeline validation)
Amazon Kinesis (real-time pipeline testing)

Advanced proficiency in SQL for data validation and reconciliation
Programming skills in Python (preferred)
Testing Expertise
Experience in: Data reconciliation (source vs target)
Data quality frameworks and validation techniques
Automated data testing tools

Understanding of medallion architecture (Raw, Silver, Gold layers)
Analytics & Reporting
Experience validating Power BI reports and datasets
Strong understanding of business KPIs and reporting consistency
Domain Expertise (Preferred)
Experience in Insurance domain (Policy, Claims, Billing data)
Familiarity with regulatory reporting, audit, and reconciliation requirements
Experience
8–12 years in QA / Data Testing / ETL Testing
3+ years in QA leadership or lead role
Experience working on enterprise-scale data platforms

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We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.