CLOUD DATA TESTING ENGINEER

About the role

The Cloud Data Testing Engineer is responsible for ensuring the quality, accuracy, and reliability of cloud-based data platforms and pipelines. The role focuses on validating data integrity, automating testing processes, and ensuring cloud data solutions are scalable, high-performing, and production-ready.

Key responsibilities

• Develop and execute test strategies for cloud data pipelines, integrations, and AWS-based solutions.
• Validate data quality, integrity, and transformation across the data lifecycle.
• Design and maintain automated testing frameworks using Python and SQL.
• Verify data models, cloud infrastructure, and pipeline orchestration to ensure reliability and performance.
• Collaborate with cross-functional teams to identify, troubleshoot, and resolve data quality issues.
• Document test results, provide quality metrics, and support continuous improvement initiatives.

About you

• At least 5 years in a data engineering role working on validation of data integration, processing and transformation pipelines and cloud infrastructure.
• Strong Python skills for test frameworks, API integration, and data validation, emphasizing code quality and TDD.
• Demonstrated proficiency with SQL for deep-dive data reconciliation, data profiling, and backend verification.
• Practical knowledge of big data storage formats (Apache Parquet, ORC), including schema validation, file size optimization testing, and identifying data corruption pitfalls.
• Demonstrated client-facing experience, multi-cultural team experience, technical leadership, team leadership.
• Experience reviewing and verifying various data models for structural integrity and performance: Relational modeling, Dimensional modeling, NoSQL modeling.
• Working knowledge of IaC on AWS (CloudFormation or CDK).
• Working knowledge of AWS Services: Glue, IAM, Lambda, DynamoDb, Step Functions, S3, CloudFormation or CDK.
• Skilled in end-to-end data test planning, scenario design, automated script writing, execution, and quality reporting.
• Experience validating the orchestration and failure boundaries of data flows and pipelines.