USA_Developer

· Execute data quality checks on large-scale structured and semi-structured datasets. · Perform schema, metadata, and Delta Lake validations. · Write complex SQL queries for backend validation and data integrity testing. · Validate CDC (Change Data Capture) and incremental data loading processes. · Test batch and streaming data pipelines. Databricks Testing · Validate Databricks notebooks, Workflows, Jobs, Delta Live Tables, and Unity Catalog implementations. · Test Bronze, Silver, and Gold layer data transformations. · Verify workflow orchestration and dependency handling across Databricks jobs. · Perform performance validation of Spark and PySpark transformations. · Validate data quality frameworks implemented in Databricks. AI/ML Testing · Validate AI/ML model integrations within enterprise applications. · Verify model scoring results and prediction outputs. · Perform data drift, schema drift, and feature validation testing. · Validate feature engineering pipelines and training datasets. · Execute model input/output verification against expected business outcomes. · Validate explainability, traceability, and auditability requirements. · Test model deployment workflows integrated through MLflow, Databricks ML, Azure ML, or similar platforms. · Validate GenAI/RAG workflows, prompt-response accuracy, and grounding mechanisms where applicable. Integration Testing · Perform end-to-end integration testing across: o Databricks o Data warehouses o APIs o AI/ML services o Cloud platforms o Downstream reporting systems · Validate data flow between source systems, data lakes, ML models, and consuming applications. · Perform contract testing and API validation. Test Automation · Design and develop automation frameworks for: o ETL testing o API testing o Data validation o ML model validation · Build reusable test utilities using Python. · Integrate automated tests into CI/CD pipelines. · Develop automated quality gates for deployment validation. · Generate automated execution reports and test metrics.

6+ years of experience in ETL/Data Testing. 3+ years of experience testing Databricks or Spark-based platforms. Hands-on experience in AI/ML Model Testing or MLOps validation. Strong coding experience in Python and SQL. Must Have Technical/Functional Skills ETL & Data Testing · Validate end-to-end ETL/ELT pipelines across enterprise data platforms. · Perform Source-to-Target validation and reconciliation testing., Skills: HiTech : Automation Testing Experience Required: 8-10 years Skills: Category Name Required Importance Experience SkillCategoryTest1_MN HiTech : Automation Testing Yes 1 7 years