We are looking for a QA Engineer with strong experience in data platform validation to ensure quality, accuracy, and reliability of data pipelines and lakehouse architecture built on AWS and Snowflake. The ideal candidate will have hands-on experience testing ETL/ELT workflows, big data systems, and data quality frameworks.
Key Responsibilities:
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Design and execute test strategies for data pipelines built using PySpark and orchestrated via Airflow.
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Validate data ingestion, transformation, and storage in S3-based lakehouse (Iceberg tables).
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Perform data reconciliation between source systems, lakehouse, and Snowflake business layer.
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Develop automated test frameworks for large-scale data validation.
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Ensure data quality, completeness, consistency, and performance across pipelines.
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Collaborate with data engineers and stewards to enforce data quality rules and governance standards.
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Identify, track, and resolve defects in data workflows and reporting layers.
Required Skills:
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5+ years of QA experience with a focus on data platforms or ETL testing.
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Strong SQL skills and experience validating large datasets.
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Experience with PySpark-based data validation or big data testing tools.
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Familiarity with Airflow workflows and AWS data ecosystem (S3).
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Understanding of Snowflake data validation and performance testing.
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Experience with test automation frameworks.
Preferred:
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Experience with data quality tools and governance platforms (e.g., Alation).
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Knowledge of Iceberg or lakehouse architectures.
We are looking for a QA Engineer with strong experience in data platform validation to ensure quality, accuracy, and reliability of data pipelines and lakehouse architecture built on AWS and Snowflake. The ideal candidate will have hands-on experience testing ETL/ELT workflows, big data systems, and data quality frameworks.
Key Responsibilities:
-
Design and execute test strategies for data pipelines built using PySpark and orchestrated via Airflow.
-
Validate data ingestion, transformation, and storage in S3-based lakehouse (Iceberg tables).
-
Perform data reconciliation between source systems, lakehouse, and Snowflake business layer.
-
Develop automated test frameworks for large-scale data validation.
-
Ensure data quality, completeness, consistency, and performance across pipelines.
-
Collaborate with data engineers and stewards to enforce data quality rules and governance standards.
-
Identify, track, and resolve defects in data workflows and reporting layers.
Required Skills:
-
5+ years of QA experience with a focus on data platforms or ETL testing.
-
Strong SQL skills and experience validating large datasets.
-
Experience with PySpark-based data validation or big data testing tools.
-
Familiarity with Airflow workflows and AWS data ecosystem (S3).
-
Understanding of Snowflake data validation and performance testing.
-
Experience with test automation frameworks.
Preferred:
-
Experience with data quality tools and governance platforms (e.g., Alation).
-
Knowledge of Iceberg or lakehouse architectures.
Graduate in Computer Science, Data Science, Information Technology, or a related field. 2-3 years of experience in data quality or data management.
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