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
Requirements:
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, AWS Glue, PySpark Deequ
Job description
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
