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Jobs via Dice • United States
Role & seniority: Quality Assurance (QA) Data Engineer, 3-5+ years experience
Stack/tools: Google Cloud Platform (BigQuery, Dataflow, Dataproc, Cloud Storage), SQL, ETL/ELT, data modeling, data validation frameworks; Python; Jira, Confluence, SharePoint; automated testing tools (e.g., Great Expectations, dbt tests)
Define quality requirements with Data Engineers, Analysts, and stakeholders; document test cases and data validation rules
Develop and execute test cases for ETL/ELT, data transformations, and ingestion; perform data validation and regression testing
Monitor, troubleshoot, and document data issues; support root-cause analysis and defect resolution; contribute to automated testing scripts and post-release validation
3-5+ years in data engineering, data testing, or QA
Proficiency in SQL and data validation frameworks; understanding of ETL/ELT processes, data modeling, and schema design
Experience with automated data testing frameworks (e.g., Great Expectations, dbt tests)
Familiarity with GCP data services (BigQuery, Dataflow, Dataproc, Cloud Storage) and Python
Programming/scripting (Java, Python); relevant software testing certifications
Location & work type: Remote (EST hours); 6-month contract with potential extension based on performance; limited interviews (up to two 45-minute sessions)
Dice is the leading career destination for tech experts at every stage of their careers. Our client, DataEdge Consulting, Inc., is seeking the following. Apply via Dice today!
Immediate need for a Quality Assurance (QA) Data Engineer reporting to Enterprise Data Platform (EDP) Delivery teams.
The initial contract is 6 months with the possibility of an extension based on performance.
Location of assignment can be remote only but working EST hours.
There will be a maximum of (2) 45-minute candidate interviews.
Key is Google Cloud Platform experience
This role has lots of development and QA tasks, not just a pure QA role.
This role involves developing and implementing test strategies, ensuring data accuracy Application performance and integrity, and optimizing data pipelines within the Google Cloud Platform (Google Cloud Platform) ecosystem. The ideal candidate will have a strong background in data engineering, test automation, and cloud technologies, with expertise in SQL and general understanding of BigQuery and Dataflow. Familiarity with LL Bean data and business process, along with proficiency using software management and collaboration tools such as Jira, Confluence, and SharePoint.
Partner with Data Engineers, Analysts, and business stakeholders to define quality requirements.
Document test cases, data validation rules, and best practices for scalable data governance.
Develop and implement test cases for ETL/ELT pipelines, data transformation, and ingestion processes.
Perform data validation, execute test cases (manual or automated) and analyze results. Regression testing ensures sufficient error validation is present. Reconcile variances and data anomalies to ensure high-quality data.
Validate data transformations and ingestion processes for structured and unstructured data.
Monitor and troubleshoot data issues, failures, and inconsistencies across the pipeline.
Provide support for root cause analysis and resolution of data-related defects, including the identification of code changes.
Document and track defects, providing detailed reports to development teams for resolution.
Participate in the design and implementation of automated testing scripts to improve testing efficiency.
Conduct regression testing to ensure that new code changes do not adversely affect existing functionality.
Perform post-release and post-implementation validation of software performance in production environments.
Continuously monitor and evaluate the quality of software deliverables, providing feedback for improvement opportunities.
Collaborate with end users to gather feedback.
3-5+ years of experience in data engineering, data testing, or quality assurance.
Proficiency in SQL, and data validation frameworks. (test strategies).
An understanding of ETL/ELT processes, data modeling, and schema design.
Familiarity with automated testing frameworks for data (e.g., Great Expectations, dbt tests).
Familiarity with Google Cloud Platform data services (BigQuery, Dataflow, Dataproc, Cloud Storage) and Python.
Strong understanding of software development and testing methodologies.
Excellent analytical and problem-solving skills.
Attention to detail and ability to document defects accurately.
Highly collaborative. Effectively works with cross-functional teams for the support and performance of the EDP and packaged applications.
Strong understanding of project management methodologies, including Agile and Waterfall.
Effective communication skills, both written and verbal, to collaborate with cross-functional teams.
Familiarity with programming languages (e.g., Java, Python) and scripting for automated testing.
Ability to work independently and manage multiple tasks simultaneously.
Google Cloud Platform or other relevant certification in software testing. Show more Show less