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QA Engineer
- Insight Global (Miami, FL)
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Job Description
This person will focus on validating data correctness, pipeline reliability, performance, security and operational readiness across ETL/transformation jobs, data stores, and consumption layers. You will work closely with data engineers, product owners and platform teams to build automated data tests, define quality gates, and ensure production-grade data delivery. This role include sperforming performance and scalability testing for queries and jobs on Oracle Exadata and Databricks clusters and work with developers and platform engineers to automate test runs in CI/CD and validate deployment artifacts and migrations.
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Skills and Requirements
• 3+ years of experience in QA or testing roles with a focus on data platforms, ETL testing or data pipelines.
• Strong SQL skills (complex joins, window functions, analytics) and experience testing in RDBMS environments — hands-on Oracle SQL and PL/SQL experience preferred.
• Hands-on experience with Databricks and Spark (PySpark) including testing notebooks, jobs and Delta Lake artifacts.
• Practical experience with Azure data services (Azure Databricks, ADLS Gen2, Azure Data Factory / Data Lake concepts).
Experience with test automation using scripting languages (Python preferred) and test frameworks/tools for data validation (pytest, unittest, or specialized tools).
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