Teams at Nexaminds

Leads enterprise quality engineering and test automation strategy across UI, API, integration, performance, and data layers. Architects Java-based automation frameworks, integrates quality gates into CI/CD pipelines, and drives AI/LLM adoption for test generation, debugging, self-healing, test data creation, and defect analysis. Establishes ETL and data validation practices, governs DevOps quality standards, mentors QA teams, and collaborates with Engineering, DevOps, Data Engineering, and Product stakeholders to improve quality maturity and delivery predictability.

Design and lead scalable data engineering solutions across Databricks and Microsoft Azure. Define enterprise data modeling standards, architect pipelines and integrations, establish engineering best practices, troubleshoot complex platform issues, and guide modernization and cloud migration initiatives. Provide technical leadership and mentorship while ensuring data platforms meet security, quality, governance, reliability, performance, and maintainability standards.

Design, develop, and maintain scalable data pipelines and models in Databricks and Microsoft Azure. Perform data ingestion, transformation, integration, migration, and modernization across enterprise data sources. Monitor and optimize pipeline performance, reliability, and data quality. Translate business requirements into technical solutions, collaborate with architects and engineers, follow security and governance standards, participate in code reviews, and document data platforms and processes.