Required Qualifications ▸ 2–4 years of experience in software automation testing, QA engineering, or a closely related discipline. ▸ Solid understanding of automation testing principles — test design, test pyramid, boundary conditions, regression strategy, and defect lifecycle. ▸ Proficiency in at least one scripting/programming language (Python strongly preferred) for writing test scripts and automation frameworks. ▸ Hands-on experience or strong conceptual understanding of data platforms and big data processing (Databricks, Spark, PySpark, or similar). ▸ Familiarity with SQL and the ability to write complex queries for data validation and reconciliation. ▸ Exposure to CI/CD tools and practices (GitHub Actions, Jenkins, GitLab CI, or equivalent). ▸ Strong problem-solving skills and the ability to work independently — define the problem, design the solution, and deliver it. ▸ Good communication skills; able to document findings clearly and raise risks effectively. Preferred Qualifications ▸ Direct experience with Databricks — notebooks, Delta Lake, Unity Catalog, or Databricks Workflows. ▸ Exposure to AI/ML validation concepts — model evaluation metrics, regression testing for ML models, or prompt/output validation for LLM-based systems. ▸ Experience with data quality tools such as Great Expectations, dbt tests, Soda, or custom-built validation frameworks. ▸ Familiarity with healthcare data standards (HL7, FHIR, claims data, ADT feeds) — a plus, not a must. ▸ Experience with API testing tools (Postman, RestAssured, pytest-httpx) to validate data interfaces and service contracts. ▸ Understanding of cloud data environments (AWS S3, Azure Data Lake, GCP BigQuery) and how data flows through them. ▸ Exposure to version control best practices, code review culture, and engineering documentation.