Jobs at Nexaminds

Build and maintain scalable data pipelines, ETL/ELT processes, data models, metric definitions, and data infrastructure. Develop production solutions with Python, SQL, Spark, dbt, Fivetran, Airflow, Databricks, Azure, and cloud storage. Collaborate with data scientists, engineers, analysts, and stakeholders to deliver reliable, accessible data and self-service tools. Troubleshoot pipeline issues, improve data quality and scalability, and use AI-assisted development tools to accelerate engineering work.

Lead an internal developer platform and productivity engineering team in a player-coach role. Manage distributed teams, sprint delivery, performance, hiring, and stakeholder communication while contributing to architecture, technical decisions, coding, and platform delivery. Drive cloud infrastructure, CI/CD, developer tooling, agile execution, and adoption of AI-assisted development tools. Partner with product, DevOps, and QA teams to manage capacity, dependencies, risks, and roadmap execution.

Design, build, operate, and scale change data capture pipelines using Debezium, Kafka or Azure Event Hubs, and Databricks. Manage streaming data across hundreds of databases and thousands of tables, ensuring reliability, low latency, schema consistency, and data quality. Troubleshoot replication lag, schema drift, backpressure, and recovery issues. Collaborate with backend and data teams on schemas and SLAs while documenting architecture and operational procedures.

Lead hands-on modernization of a live Vue and PHP application. Build responsive, accessible, and performant interfaces; improve component architecture, CSS, state management, and front-end quality; integrate RESTful APIs; review code; resolve UI performance issues; and collaborate with product and UX teams. Use AI-assisted development tools to accelerate delivery and ramp quickly on legacy codebases.

Lead hands-on modernization of a live Vue and PHP application by converting legacy jQuery pages into accessible, responsive Vue components. Establish front-end architecture, component standards, CSS and state-management patterns, performance practices, testing, and code-review quality. Collaborate with product and UX teams, integrate RESTful APIs, resolve UI bottlenecks, and use AI-assisted development tools to accelerate delivery and navigate legacy code.

Build and enhance data engineering frameworks, scalable data solutions, centralized ETL logic, metric definitions, and concise data models. Develop self-service data tools supporting data science, internal applications, and organizational decision-making. Collaborate with cross-functional stakeholders, use AI coding agents such as Claude Code or Codex, and write production-level Python and SQL.

Designs and maintains CI/CD pipelines, automates cloud infrastructure with IaC, manages Kubernetes and GCP/GKE environments, and implements progressive delivery, observability, alerting, and incident response. The role partners with Engineering, QA, and Product teams to improve reliability, scalability, security, deployment velocity, and operational efficiency while troubleshooting production issues and promoting DevOps, SRE, and GitOps practices.

Leads end-to-end technology projects in healthcare, coordinating Engineering, Product, QA, Infrastructure, and business teams. Manages project plans, timelines, budgets, risks, dependencies, resources, stakeholder communications, Agile ceremonies, governance, compliance, and delivery quality. Drives issue resolution, executive reporting, and continuous improvement across multiple concurrent initiatives.

Design, build, deploy, and scale Python-first backend services, REST APIs, data pipelines, and cloud-native applications on GCP. Maintain and enhance Java services, integrate AI and LLM capabilities, modernize infrastructure, and ensure application security, reliability, and high test coverage. Collaborate with product, data, and infrastructure teams while conducting code reviews and documenting technical solutions.

Lead end-to-end technology projects in healthcare from planning through implementation. Coordinate Engineering, Product, QA, Infrastructure, and business teams; manage schedules, budgets, resources, risks, dependencies, and stakeholder communications; facilitate Agile ceremonies; ensure quality, compliance, documentation, and successful delivery; and drive continuous improvements across project processes and collaboration.

Design, develop, test, and maintain native iOS applications using Swift. Integrate RESTful APIs, collaborate with Java backend and React web teams, and contribute to architecture, code reviews, Agile planning, testing, CI/CD, and App Store releases. Troubleshoot performance, stability, and production issues while improving application quality and maintainability. Communicate technical progress and tradeoffs with cross-functional stakeholders.

Design, develop, and maintain native Android applications using Kotlin and Android Studio. Integrate mobile apps with RESTful APIs and backend services, collaborate with Java backend and React teams, troubleshoot production issues, participate in architecture discussions and code reviews, support releases and CI/CD improvements, and communicate technical progress and tradeoffs.

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.

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.

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.