Company Description
Education
Job Description
USC and GC only (No H1B, OPT, CPT at this time)
Data Quality Engineer (AWS Data Platform) Overview We are seeking a highly skilled Data Engineering - Quality Engineer 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.
Key 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 Qualifications
- 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
Additional Information
All your information will be kept confidential according to EEO guidelines.
