DE&A -Data Engineer ETL Developer (Snowflake)

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

• 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
• 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

• 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
• 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
• 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
• 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
• 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