Jobs at Nexaminds

Principal Data Engineer responsible for developing and supporting Databricks pipelines, integrating heterogeneous healthcare data sources, modeling unified data products, and ensuring data quality and availability. The role partners with business stakeholders, product teams, data engineers, and clinical leaders to gather requirements, create datasets, support ad hoc analytics, enable machine learning and AI use cases, and assist with acquisition integrations. Extensive healthcare data, cloud, SQL, PySpark, and Python experience is required.

Leads enterprise quality engineering and test automation strategy across UI, API, integration, performance, and data layers. Architects Java-based automation frameworks, establishes CI/CD quality gates, drives ETL and data validation, and governs responsible AI adoption in QA. The role leads QA transformation initiatives, mentors engineers, collaborates with Engineering, DevOps, Data, and Product teams, and evaluates AI-enabled testing capabilities.

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, drives ETL and data validation, and governs responsible AI adoption in QA. Mentors QA engineers, establishes standards and governance, leads transformation initiatives, and collaborates with Engineering, DevOps, Data Engineering, and Product teams.

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.

Designs, develops, and maintains scalable data pipelines and models in Databricks and Microsoft Azure. Responsibilities include data ingestion, transformation, integration, performance optimization, troubleshooting, migration, governance, documentation, and collaboration with architecture and engineering teams. The role requires translating business needs into reliable technical data solutions and supporting analytical and operational use cases.

Design and lead scalable data engineering solutions using Databricks and Azure. Establish enterprise data modeling and engineering standards, architect pipelines and integrations, improve platform reliability and performance, guide modernization and cloud migrations, troubleshoot complex issues, and mentor data engineers while ensuring security, quality, and governance.

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.

Lead the development, optimization, and scaling of production-grade data ingestion and transformation pipelines. Design bulk onboarding, backfills, migrations, and event-driven data processes with monitoring, idempotency, rollback plans, data quality controls, reconciliation, anomaly detection, and auditability. Collaborate with Platform and SRE teams to improve production safety and observability, while producing runbooks, cutover checklists, validation reports, and post-mortem follow-ups.

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.

Design and lead scalable data engineering solutions across Databricks and Microsoft Azure. Establish enterprise data modeling and engineering standards, oversee pipelines and integrations, evaluate architectures, improve platform reliability and performance, mentor engineers, troubleshoot complex issues, support cloud modernization, and ensure security, quality, and governance compliance.

Design, develop, and maintain scalable data pipelines, models, ingestion, transformation, and integration solutions using Databricks and Microsoft Azure. Troubleshoot performance and data quality issues, optimize workflows, support migrations and modernization, and collaborate with architecture and engineering teams. Translate business requirements into technical solutions while following security, governance, quality, and engineering best practices.

Design, build, and deploy production-grade AI applications, agents, retrieval systems, integrations, and workflows for enterprise customers. Lead technical discovery, architecture workshops, prototyping, troubleshooting, deployments, and go-live support. Translate customer needs into scalable solutions and reusable engineering patterns, while providing product feedback that informs roadmaps. Partner with Product, Sales Engineering, Customer Success, and Engineering teams, and mentor customer-facing engineers. Travel to customer sites as needed.

Build and improve cloud-native infrastructure, internal developer tooling, platform-as-a-service capabilities, CI/CD workflows, and Infrastructure-as-Code practices. Collaborate with platform and application engineers to improve developer experience, deployment reliability, observability, and productivity. Troubleshoot infrastructure issues, adopt AI-assisted development tools, explore LLM-powered operational solutions, and develop modern platform engineering skills through mentorship.

Develop and maintain cross-platform mobile applications using .NET MAUI and C#. Integrate RESTful APIs and backend services, manage SQL-based data persistence, optimize mobile performance, and troubleshoot asynchronous lifecycle issues. Collaborate with UX designers and backend engineers to deliver intuitive, reliable applications.

Administer Microsoft SCCM/MECM for application deployment, patch management, OS imaging, upgrades, and endpoint troubleshooting. Maintain site health, distribution points, and servers; investigate CM and Windows logs; perform remote fixes; create collections using WQL; and support remediation efforts with security and application teams across global sites.

Lead the design, development, and maintenance of scalable test automation frameworks for UI, API, and integration testing. Develop performance-testing strategies, execute automated and load tests, analyze results, and resolve bottlenecks with engineering teams. Lead testing efforts, improve test coverage, manage defects, support Agile planning, collaborate cross-functionally, and mentor junior QA engineers while advocating for quality and process improvements.

Lead enterprise and domain data modeling across conceptual, canonical, logical, physical, and business models. Translate business concepts into scalable data structures, facilitate stakeholder workshops, establish business glossaries, and align models with governance, metadata, MDM, analytics, AI, reporting, and data stewardship initiatives. Support engineering teams implementing cloud and lakehouse solutions while serving as a domain architecture lead for durable, flexible modern data platforms.

Design, build, deploy, and troubleshoot production-grade AI applications, integrations, pipelines, and agentic workflows for customers. Lead technical discovery, workshops, architecture discussions, prototyping, and go-live support. Translate customer needs into reliable systems and product feedback, establish reusable engineering patterns, collaborate across Product, Sales Engineering, Customer Success, and Engineering, and mentor customer-facing engineers. Travel to customer sites as needed.

Design, build, optimize, and maintain scalable Databricks data pipelines and ETL/ELT workflows. Manage CI/CD with Azure Pipelines and Azure Key Vault, improve performance and cost efficiency, implement data quality and governance practices, troubleshoot distributed systems, collaborate with stakeholders, and support cloud data platform modernization initiatives.

Design, build, and deploy production-grade AI applications, integrations, pipelines, and agentic workflows for customers. Lead discovery, prototyping, architecture workshops, technical troubleshooting, and customer go-lives. Integrate LLMs, retrieval systems, tools, and orchestration into enterprise environments, establish reusable engineering patterns, mentor customer-facing engineers, and translate customer feedback into product and engineering priorities.