Top Tech Jobs & Startup Jobs

Develop and maintain frontend and backend components of the Hickory application. Build React and NextJS interfaces, backend services, APIs, user management workflows, provisioning automation, data mapping functionality, and cloud integrations. Implement secure, scalable code while working with databases, authentication, authorization, and Azure services.

Designs and maintains data pipelines using ETL/ELT platforms, develops dbt models, and manages Snowflake workloads. Establishes data quality testing and monitoring, optimizes data performance and costs, supports CI/CD and infrastructure-as-code practices, participates in architecture reviews, and mentors junior engineers. Collaborates with analysts and data scientists to build reliable data models and transformation logic.

Lead end-to-end delivery of an enterprise cloud data and AI platform. Design data architectures, ingestion pipelines, models, and machine-learning systems; productionize supervised, unsupervised, deep-learning, and generative AI solutions; establish MLOps, explainability, monitoring, and CI/CD practices; and lead distributed engineering teams. Serve as the primary client-facing technical liaison, driving architecture, governance, documentation, UAT, cutover, and production readiness.

Lead development of production agentic AI applications, translating business outcomes into agent specifications, tools, evaluations, and acceptance criteria. Build retrieval, orchestration, structured-output, approval, fallback, and failure-handling workflows using LLMs and APIs. Own evaluation harnesses, lead agile ceremonies, estimate work, and create sprint plans. Develop applications with Python or Java/Spring Boot and modern front-end technologies, while providing technical leadership and client-ready communication.

Lead the vision, strategy, roadmap, and delivery of an enterprise AML monitoring data platform. Own transaction and party data models, detection rules, alert and case data, risk scoring, governance, quality, analytics, and AI/ML use cases. Partner with compliance, investigations, risk, data engineering, architecture, governance, and analytics teams to define requirements, manage Agile delivery, establish metrics, and improve detection performance, adoption, and business impact.

Build and maintain CI/CD pipelines, implement Terraform-based Infrastructure as Code, and support Kubernetes container orchestration for cloud-native data solutions. Collaborate with cloud and data engineering teams, provide production support and incident response, and improve monitoring and observability. Preferred experience includes GitOps tools such as ArgoCD or Flux, Kubernetes cluster management, DevSecOps, compliance automation, and FinOps.

Build and maintain CI/CD pipelines, implement Infrastructure as Code with Terraform, support Kubernetes and cloud platforms, respond to production incidents, and improve monitoring and observability. Collaborate with Cloud Solutions and Data Engineering teams to enable secure, repeatable delivery of cloud-native data solutions. Experience with GitOps, Kubernetes cluster management, DevSecOps, compliance automation, and FinOps is advantageous.

Lead Snowflake architecture, administration, governance, performance and cost optimization across enterprise data warehouse environments. Implement RBAC and compliance controls, design replication and disaster recovery strategies, and manage production incident response. The role requires extensive data platform or database engineering experience, Snowflake production administration, high-availability or disaster recovery expertise, cloud data platform experience, and familiarity with Databricks, ETL platforms, Infrastructure as Code, and regulated environments.

Design, develop, and deploy scalable cloud-based data pipelines and ETL/ELT processes. Build and optimize Snowflake and dbt solutions, apply data warehousing concepts such as dimensional modeling, CDC, and SCDs, and use workflow orchestration tools. Ensure data governance, security, and privacy best practices while collaborating with technical and non-technical stakeholders in an Agile environment.

Lead end-to-end delivery of scalable AI and GenAI applications, owning architecture, performance, agentic workflows, tool orchestration, and RAG systems. Review prompts, code, and application architecture; mentor AI engineers; conduct technical reviews; and collaborate with stakeholders to translate business needs into production solutions. Drive best practices for LLMOps, observability, reliability, and cloud-native design.