Senior Data Engineer (AWS Data Platform)

Ideal Candidate Profile: We are seeking a highly skilled Senior Data Engineer (AWS Data Platform) to define and implement end-to-end testing strategies for a modern data platform built on AWS. This role will be responsible for ensuring data quality, reliability, and performance across the entire pipeline; from ingestion to transformation and reporting.

• Job Duties & Responsibilities
• Define the end-to-end testing scope based on solution architecture and project documentation
• Design and implement a comprehensive testing strategy and plan aligned with organizational QA standards
• Develop and maintain test scripts and frameworks for the Redshift serverless platform
• Perform testing across key technologies, including:
• AWS Redshift
• AWS DMS (Data Migration Service)
• AWS Glue
• PySpark Deequ
• Event Bridge
• Data Lakes
• Python-based data pipelines
• Apache Airflow
• dbt (data build tool)
• Build and implement automated testing solutions to ensure:
• End-to-end data validation
• Data ingestion accuracy
• Transformation logic integrity
• Data pipeline reliability
• Conduct test coverage analysis and ensure adequate validation across all data engineering workflows
• Prepare and manage test data
• Review and provide feedback on:
• Solution architecture
• Data models
• Design and technical documentation
• Collaborate with cross-functional teams (Data Engineering, BI, DevOps, Product) to:
• Identify testing impacts
• Mitigate risks
• Ensure high-quality deliverables

• Required Skills & Experience
• Proven experience in data engineering testing / data QA / ETL validation
• Strong hands-on experience with AWS data services (Redshift, Glue, DMS)
• Proficiency in Python for test automation and validation
• Experience with Airflow and orchestration testing
• Hands-on experience with dbt and data transformation validation
• Familiarity with CDK for infrastructure validation
• Experience in BI testing in Quicksuite will be highly beneficial
• Experience with data quality tools such as PySpark Deequ or similar
• Strong understanding of:
• Data warehousing concepts
• ETL/ELT pipelines
• Data validation techniques (schema, reconciliation, anomaly detection)

• Preferred Qualifications
• Experience designing enterprise-level test strategies for data platforms
• Knowledge of CI/CD pipelines for data and test automation
• Experience working in Agile / Scrum environments
• Familiarity with data observability frameworks