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 (35 years).
- 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.
Highly Desired / 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.
Job Title: QA Data Engineer with GCPLocation: Remote (EST Hours) Duration: 12 Months ContractMandatory 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 t...
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 (35 years).
- 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.
Highly Desired / 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.
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