Data Lakehouse QA Automation Engineer #3630886

Be Part Of A High-Performing Team:

Join a collaborative technology team building a modern cybersecurity data platform that centralizes critical data across insider risk, access management, privacy, data loss prevention, phishing, smishing, and related security domains. This team is standing up a new enterprise data lakehouse environment designed to create a more reliable, authoritative source for cybersecurity data. The work is highly cross-functional, fast-moving, and suited for someone who enjoys helping build structure, testing practices, and quality standards in a developing environment rather than simply maintaining a mature process.

What’s In Store For You:

This is a contract-to-hire opportunity with strong long-term intent for someone who can make an immediate impact. The role follows a hybrid schedule with two days onsite per week, ideally Wednesday and Thursday, in either Charlotte, NC or Jersey City, NJ. Engagement: W2 only, no C2C or 1099.

How You Will Make An Impact

• Support end-to-end testing for a newly built cybersecurity data lakehouse environment.
• Test ETL/data pipelines across Databricks, Spark SQL, Python, PySpark, and related modern data platform components.
• Build and execute test scenarios for data ingestion, transformation, validation, reporting, and downstream consumption.
• Validate source-to-target data flows and identify issues across complex data pipelines.
• Support automation testing efforts for data pipelines and recurring validation processes.
• Partner with data engineering, governance, reporting, and stakeholder teams to ensure data quality, accuracy, and completeness.
• Help define testing practices, test cases, and QA processes in a growing environment that is still being matured.
• Support validation of Power BI reporting outputs tied to cybersecurity data.

Are you an experienced data QA professional ready to make an impact in modern data platform testing?

• 5–10 years of experience in QA, data testing, ETL testing, data validation, or related data quality testing roles.
• Hands-on experience testing within Databricks environments.
• Strong SQL and Spark SQL skills, with the ability to validate data across source, transformation, and target layers.
• Hands-on Python and PySpark experience.
• Experience using Pandas for data validation, analysis, or testing support.
• Proven experience with ETL/data pipeline testing and source-to-target validation.
• Experience building or contributing to automation testing frameworks.
• Ability to explain end-to-end testing strategy, not just execute existing test scripts.
• API testing and Postman experience are helpful, but may be learned if the candidate is otherwise strong.
• Strong communication skills with the ability to collaborate across technical teams, stakeholders, peers, and leadership.
• Comfortable working in a newer environment where processes, test cases, and frameworks may need to be built from scratch.
• Financial services or cybersecurity data experience is a plus, but not required if technical skills are strong

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