Top Tech Jobs & Startup Jobs

Build and operate data platform services, APIs, connector frameworks, ingestion and synchronization capabilities. Improve query and data movement performance through partitioning, parallelism, and resource management. Develop orchestration, retries, checkpointing, and observability for reliable workloads. Collaborate with product and engineering teams to design interfaces, make architectural decisions, and deploy production-ready data infrastructure supporting enterprise applications, analytics, and AI.

Build and operate data platform services for enterprise integrations, including data access, query execution, ingestion, synchronization, connector frameworks, orchestration, retries, checkpointing, and observability. Optimize distributed data workloads for performance, reliability, and resource efficiency. Design reusable production systems and APIs in collaboration with product and engineering teams.

Own end-to-end test strategy and automation across the SDLC. Design test cases and plans, implement integration, system, regression, and performance testing, integrate tests into CI/CD pipelines, partner with developers on quality and acceptance criteria, investigate failures, and report quality metrics. The role may also support Kubernetes workloads, SOC 2 evidence, and regulated environments.

Secure the product throughout its lifecycle by partnering with developers on security design and architecture reviews, threat modeling, penetration testing, secure code reviews, and security standards. Operate SAST, DAST, dependency, and supply-chain scanning tools; triage findings and guide remediation. Contribute security evidence to SOC 2 and ISO 27001 compliance efforts while improving developer security practices.

Evaluates technical control design and effectiveness, validates compliance evidence, performs technology risk assessments, and supports AI governance aligned with ISO 42001. Partners with Engineering, Security, Product, and business teams on compliance improvements, FedRAMP activities, audits, customer assurance, metrics, dashboards, and automation. The role translates technical risks into practical compliance guidance and improves the organization’s assurance program.

Designs and operates production-grade AI agent infrastructure across Azure, Oracle Cloud, GCP, and AWS. Owns Kubernetes, Terraform, CI/CD, GitOps deployments, observability, security, reliability, networking, and reusable automation frameworks. Automates provisioning, secret rotation, compliance checks, releases, and operational runbooks while partnering with product and platform engineers to support secure, portable enterprise environments.

Lead enterprise architecture for an Oracle Fusion Cloud ERP transformation, including Phase 0 discovery, target-state architecture, process master list ownership, integration design, and cross-functional value-stream mapping. Partner with stakeholders and AI engineers to define Oracle Fusion, PaaS, integration, data, analytics, identity, and security models. Advise on inventory architecture and coordinate related PLM, WMS, EPM, payroll, HCM, tax, procurement, and e-commerce initiatives.

Lead the Logistics and Supply Chain Management workstream for a greenfield Oracle Fusion Cloud implementation. Facilitate business workshops, configure Inventory Management, Order Management, and Procurement, own the Indirect Procurement value stream, design item master structures, support legacy data migration, and lead integrations with Shopify, warehouse management systems, and related Oracle modules. Engage directly with client stakeholders and leadership in business English while maintaining requirements, configuration, and testing documentation.

Own AI-driven enterprise product features from discovery and strategy through execution, iteration, adoption, and measurable business impact. Define roadmaps, requirements, success metrics, and engineering backlogs; prioritize AI use cases by ROI, feasibility, risk, and time-to-value. Partner with engineering, data science, sales, marketing, delivery, and customers to build B2B products across enterprise platforms, support implementations, and drive product positioning and launches.

Design, build, and maintain Python backend services, REST APIs, microservices, data pipelines, and enterprise integrations. Optimize databases, caching, performance, and reliability while integrating AI model servers, vector databases, and workflow automation. Implement authentication and authorization, testing, monitoring, logging, and cloud deployments on AWS or GCP. Collaborate with frontend, AI, DevOps, and product teams in a fast-moving startup environment.