. Lead and manage end to end Quality Engineering (QE) and testing delivery for enterprise Data & Analytics platforms, Big Data applications, ETL/ELT pipelines, cloud data platforms, and data integration solutions.
. Define and execute comprehensive test strategies, test plans, quality standards, automation roadmaps, and release readiness criteria across multiple projects and Agile teams.
. Provide technical leadership and oversight for testing solutions built using Databricks, Apache Spark, PySpark, Python, Hadoop/HDFS, Hive, Azure Data Factory (ADF), and SQL.
. Lead ETL/ELT testing, source to target validation, data reconciliation, data quality, integration, regression, system, performance, and end to end testing.
. Oversee the design and execution of complex SQL queries and automated validation frameworks to ensure data accuracy, completeness, consistency, integrity, and transformation correctness.
. Manage and mentor onsite/offshore QE teams, including resource planning, workload allocation, performance management, technical guidance, and delivery accountability.
. Drive test automation strategy and framework development using Python, PySpark, SQL, and enterprise automation tools to improve test coverage, efficiency, and reliability.
. Oversee validation of Databricks notebooks, Spark jobs, Delta Lake tables, ADF pipelines, batch processing, and workflow orchestration, including Control M and Unix/Linux environments.
. Establish and monitor quality KPIs, test coverage, defect metrics, automation effectiveness, delivery risks, and release readiness dashboards.
. Lead defect triage, root cause analysis, risk assessment, issue escalation, and resolution coordination across development, engineering, architecture, and business teams.
. Collaborate with Product Owners, Data Engineers, Architects, Scrum Masters, business stakeholders, and program leadership to align testing priorities with delivery objectives.
. Manage testing schedules, estimates, resource capacity, dependencies, and deliverables to ensure quality milestones and project commitments are achieved.
. Champion BDD/TDD practices, continuous testing, CI/CD integration, test automation, and shift left quality engineering.
. Lead testing governance, stakeholder reporting, quality reviews, production readiness assessments, and go/no go recommendations.
. Support Agile/Scrum and SAFe delivery practices, including sprint planning, backlog refinement, PI planning, reviews, retrospectives, and cross team coordination.
Drive continuous improvement through standardized testing processes, reusable frameworks, automation, defect prevention, and quality engineering best practices.
Requirements:
. 12+ years of experience in Software Quality Engineering, ETL/Big Data Testing, Data Warehouse Testing, or enterprise application testing, including 5+ years in Test Lead/Test Manager roles.
. Proven experience managing enterprise QE/testing teams, including onsite/offshore delivery, resource planning, test execution, stakeholder communication, and quality governance.
. Strong experience defining and implementing enterprise test strategies, automation frameworks, quality standards, test plans, and release readiness processes.
. Strong technical knowledge of Databricks, Apache Spark, PySpark, Python, Azure Data Factory (ADF), Hadoop/HDFS, Hive, SQL, and ETL/ELT platforms.
. Advanced SQL and data validation expertise, including source to target reconciliation, transformation validation, data quality checks, and complex backend testing.
. Experience managing testing for large scale Data Lakes, Data Warehouses, Lakehouse architectures, cloud data platforms, and enterprise data integration solutions.
. Strong understanding of Databricks Workflows, Delta Lake, ADF pipelines, batch processing, job dependencies, Unix/Linux, and Control M or equivalent enterprise schedulers.
. Experience leading functional, integration, regression, system, performance, API, data quality, and end to end testing.
. Experience implementing test automation using Python, PySpark, SQL, Selenium, Cucumber/BDD, PyTest, or equivalent frameworks.
. Strong knowledge of Agile/Scrum, SAFe, SDLC/STLC, CI/CD, defect management, test management, and quality assurance governance.
. Proven ability to manage quality metrics, test coverage, defect leakage, delivery risks, resource capacity, and stakeholder reporting.
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