Data Quality Engineer

Pay Range: $70- $85/hr. The pay rate may differ depending on your skills, education, experience, and other qualifications.

Featured Benefits:

• Medical Insurance in compliance with the ACA.
• 401(k).
• Sick leave in compliance with applicable state, federal, and local laws.

Profile: Title: 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