Probal DasGupta
Overview
Job Title: QA Data Engineer with GCP Location: Remote (EST Hours) Duration: 12 Months Contract Mandatory Skills
Strong SQL and data validation / testing frameworks experience. Solid understanding of ETL / ELT processes data modeling and schema design. Experience in data engineering QA testing or data validation. Exposure to test automation and data quality frameworks. Excellent analytical documentation and troubleshooting skills. Knowledge of Agile and Waterfall project methodologies. Experience collaborating across cross-functional data teams. Nice-to-Have Skills
Experience with GCP data services: BigQuery, Dataflow, Dataproc, Cloud Storage. Familiarity with data testing tools such as Great Expectations or dbt tests. Working knowledge of Python for scripting and test automation. Basic understanding of Java or other programming languages. GCP or data / software testing certifications. Responsibilities
Collaborate with Data Engineers Analysts and business stakeholders to define and implement quality requirements. Document test cases data validation rules and best practices to ensure scalable data governance. Develop and execute test cases for ETL / ELT pipelines data ingestion and transformation processes. Perform data validation (manual and automated) analyze results and ensure regression testing and error handling. Validate data transformations and ingestion processes for structured and unstructured datasets. Monitor and troubleshoot data issues failures and inconsistencies across pipelines. Conduct root cause analysis for data defects and support resolution by identifying necessary code changes. Document track and report defects to development teams ensuring timely fixes and verification. Design and implement automated testing scripts to improve testing efficiency and coverage. Perform regression testing to ensure stability of existing functionality after code changes. Conduct post-release and post-implementation validation to ensure production data quality and performance. Continuously monitor and evaluate data quality providing feedback for improvement. Collaborate with end users to gather feedback and ensure alignment with business requirements. Key Skills
Apache Hive, S3, Hadoop, Redshift, Spark, AWS, Apache Pig, NoSQL, Big Data, Data Warehouse, Kafka, Scala Employment Type: Full Time Experience: years Vacancy: 1
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Job Title: QA Data Engineer with GCP Location: Remote (EST Hours) Duration: 12 Months Contract Mandatory Skills
Strong SQL and data validation / testing frameworks experience. Solid understanding of ETL / ELT processes data modeling and schema design. Experience in data engineering QA testing or data validation. Exposure to test automation and data quality frameworks. Excellent analytical documentation and troubleshooting skills. Knowledge of Agile and Waterfall project methodologies. Experience collaborating across cross-functional data teams. Nice-to-Have Skills
Experience with GCP data services: BigQuery, Dataflow, Dataproc, Cloud Storage. Familiarity with data testing tools such as Great Expectations or dbt tests. Working knowledge of Python for scripting and test automation. Basic understanding of Java or other programming languages. GCP or data / software testing certifications. Responsibilities
Collaborate with Data Engineers Analysts and business stakeholders to define and implement quality requirements. Document test cases data validation rules and best practices to ensure scalable data governance. Develop and execute test cases for ETL / ELT pipelines data ingestion and transformation processes. Perform data validation (manual and automated) analyze results and ensure regression testing and error handling. Validate data transformations and ingestion processes for structured and unstructured datasets. Monitor and troubleshoot data issues failures and inconsistencies across pipelines. Conduct root cause analysis for data defects and support resolution by identifying necessary code changes. Document track and report defects to development teams ensuring timely fixes and verification. Design and implement automated testing scripts to improve testing efficiency and coverage. Perform regression testing to ensure stability of existing functionality after code changes. Conduct post-release and post-implementation validation to ensure production data quality and performance. Continuously monitor and evaluate data quality providing feedback for improvement. Collaborate with end users to gather feedback and ensure alignment with business requirements. Key Skills
Apache Hive, S3, Hadoop, Redshift, Spark, AWS, Apache Pig, NoSQL, Big Data, Data Warehouse, Kafka, Scala Employment Type: Full Time Experience: years Vacancy: 1
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