Top AWS Jobs in Sydney

Build and ship full-stack features for the Learning Portal (Next.js UI, Go services, Supabase edge functions). Design scalable systems and infrastructure (IaC with SST/Pulumi, AWS, Vercel), implement testing (Vitest/Jest, Cypress/Playwright), perform PR reviews, maintain docs, run CI/CD, and be on-call to ensure reliable student-facing experiences.

Develop and operate scalable, reliable data platforms and pipelines across their full lifecycle. The role involves Snowflake data engineering, cloud architecture, dbt transformations, Airflow orchestration, SQL and Python development, infrastructure as code, data quality, security, governance, and cost optimization. The engineer will translate business requirements into technical solutions, provide design and code leadership, mentor engineers, collaborate with architects and product teams, and support enterprise data products across multiple business domains.

Architect, manage, and improve Nine’s Grafana-based observability platform across Kubernetes, cloud, and on-premises environments. Responsibilities include monitoring, logging, tracing, profiling, OpenTelemetry instrumentation, dashboards, alerting, incident response, infrastructure automation, security, compliance, and multi-tenant reliability. The role also mentors engineering teams and requires expertise in Kubernetes, Terraform, Helm, cloud infrastructure, CI/CD, scripting, and Grafana ecosystem technologies.

Provides technical support for technology partners and mutual customers using MongoDB and related systems. Responsibilities include troubleshooting application, database, storage, networking, and cloud issues; guiding solution integrations; coordinating multi-vendor escalations; onboarding partners; documenting architectures and known issues; and reducing interoperability risks. The role also develops partner knowledge bases, communicates best practices, supports releases, and becomes a technical specialist capable of handling advanced diagnostics and escalations independently.

Analyze external attack surface data to establish a validated exposure baseline. Consolidate and reconcile asset records, map ownership, identify unmanaged or vulnerable infrastructure, prioritize risks, and recommend remediation or risk acceptance. Collaborate with technical and business stakeholders to validate asset criticality, maintain audit-ready governance documentation, and report exposure trends and systemic control issues to cybersecurity leadership.

Administer and operate Azure Databricks platforms while enabling AI capabilities such as Model Serving, Vector Search, Lakebase, and MCP Server. Engineer Azure infrastructure, networking, security, governance, monitoring, and automation using Terraform, Bash, Azure DevOps, Git, and CI/CD. Support Spark, Delta Lake, Unity Catalog, and Lakehouse technologies, with AWS-to-Azure integration and AI/LLM platform experience considered valuable.

Develops end-to-end full-stack features for the Customer Experience team. Owns complex frontend and backend initiatives, shapes service architecture, establishes engineering standards, improves reliability and efficiency, and collaborates with product, business, and customer stakeholders. Requires strong TypeScript, React, Node.js or NestJS, web architecture, API, database, microservices, DevOps, and cloud deployment expertise.

Manage mission-critical SQL Server and PostgreSQL production databases in a 24x7 environment. Lead database reliability, performance tuning, monitoring, high availability, disaster recovery, automation, cloud migration, capacity planning, security, and compliance efforts. Mentor junior DBAs, participate in incident response, optimize database code, establish operational standards, and collaborate with engineering and infrastructure teams. Requires extensive production DBA experience, strong scripting skills, and expertise across cloud and database platforms.

Lead security operations across cloud, infrastructure, endpoints, identity, and security platforms. Manage monitoring, incident response, vulnerability remediation, security controls, audits, and continuous improvement. Provide technical leadership during incidents, collaborate with stakeholders and vendors, maintain documentation, and participate in critical-incident on-call support within a regulated financial services environment.

Design, build, test, deploy, and monitor reliable AI/ML products and services at scale. Develop real-time and batch data pipelines, optimize ranking and retrieval models using embeddings, transformers, and LLMs, and establish evaluation, observability, and model lifecycle management practices. Collaborate across product, engineering, data, and consumer experience teams while contributing to scalable ML platform capabilities.

Lead enterprise observability strategy and Dynatrace implementation across hybrid and multicloud environments. Configure application, infrastructure, user, synthetic, and log monitoring; establish governance, SLIs, SLOs, error budgets, and observability maturity roadmaps. Integrate Dynatrace with ServiceNow, CI/CD pipelines, cloud platforms, Terraform, APIs, and OpenTelemetry. Enable AIOps, automation, self-healing, proactive incident detection, and alert optimization while guiding engineering, SRE, and operations teams.

Designs, deploys, tests, and troubleshoots global network sites, circuits, switching hardware, and cloud on-ramp connections. The role develops deployment documentation and configurations, collaborates with data centers, carriers, suppliers, and global stakeholders, prototypes network solutions, analyzes root causes, and leads deployment and migration plans. Responsibilities include Cisco and Juniper switching, AWS, Azure, Oracle connectivity, fiber-optic technologies, automation, and network operations.

Build and operate production-grade AI products, APIs, RAG solutions, and agentic workflows. Own complex AI initiatives from discovery and architecture through deployment and continuous improvement. Develop shared AI frameworks and platform capabilities, establish standards for testing, evaluation, observability, security, governance, and reliability, and improve system performance and cost. Partner with Product, Engineering, Data, and Infrastructure teams while providing technical guidance, mentoring engineers, and shaping scalable AI architecture.

Leads the design, delivery, and evolution of Qantas’ enterprise data platforms. The role establishes technical strategy and engineering standards, develops scalable cloud-based data solutions, oversees reliability, security, governance, observability, and cost optimization, and drives integration across complex ecosystems. It also includes architecture reviews, production readiness, incident response, stakeholder engagement, mentoring engineers, and adoption of modern data, analytics, and AI technologies.

Develop, deploy, and improve distributed big-data pipelines for data ingestion, fusion, machine learning, and network analysis. Lead end-to-end data engineering solutions, improve scalability and reliability, support security and compliance reviews, and serve as a technical expert in customer engagements. Collaborate with engineering, data science, product, and public-sector stakeholders while evaluating tools and mentoring teams.

Leads and grows an AI Deployment Engineering team responsible for delivering agentic AI implementations, customer adoption, and measurable business outcomes. Owns portfolio roadmaps, staffing, delivery standards, technical quality, customer relationships, and cross-functional coordination. Establishes reliable, safe, auditable deployment practices and remains technically hands-on with agent architecture, evaluations, integrations, and production troubleshooting.

Designs and deploys agentic AI workflows and integrations for enterprise customers. Leads implementations from scoping through adoption, builds APIs and automations, configures data connections and guardrails, evaluates agent performance, and manages human-in-the-loop processes. Acts as the primary technical customer contact while coordinating engineering, data science, product, and customer success teams. Owns implementation roadmaps, requirements, acceptance criteria, documentation, and measurable delivery outcomes.

Own the architecture and technical direction of Ripple’s reconciliation platform infrastructure. Lead scalability, performance, security, reliability, and long-term maintainability decisions; establish standards; mentor senior infrastructure engineers; and remain hands-on by prototyping and building critical components. The role requires deep AWS and Kubernetes expertise, multi-cloud infrastructure experience, infrastructure-as-code proficiency, and experience supporting high-volume transactional workloads.

Build and own the cloud and edge platform connecting autonomous drones, docks, and customers. Responsibilities include AWS infrastructure as code, backend and desktop applications, offline-first synchronization, OTA fleet updates, CI/CD, monitoring, alerting, video streaming, and reliable networking across remote cattle stations. The role is hands-on, Sydney-based five days per week, with regular domestic and potentially international field deployments.

Build and maintain full-stack web applications and backend services using React, TypeScript, Java, and Spring Boot. Develop accessible, responsive interfaces, reusable components, REST API integrations, and microservices. Collaborate with cross-functional teams, optimize performance, troubleshoot incidents, support security and testing practices, and contribute to cloud-native platforms, CI/CD, Agile delivery, and AI-powered conversational experiences.

Build, test, operate, and maintain scalable backend services, APIs, and microservices. Collaborate with product managers, architects, data engineers, and cross-functional teams to deliver secure cloud-native solutions. Develop integrations, work with databases, caching, messaging, and distributed systems, and troubleshoot production issues. Contribute to monitoring, observability, performance optimization, code reviews, CI/CD, automation, Agile delivery, and continuous engineering improvement.

Build and maintain full-stack web applications and backend services for PUMA’s global digital ecosystem. Develop React and TypeScript interfaces, Java and Spring Boot APIs, microservices, and AI-powered conversational experiences. Collaborate with product, design, architecture, data, and engineering teams; optimize performance, accessibility, security, observability, and reliability; troubleshoot production issues; contribute to technical design and engineering standards; and mentor junior engineers.

Design, build, and operate scalable backend systems, APIs, microservices, and distributed services. Lead technical implementations, improve performance, reliability, security, observability, and operational excellence, and collaborate with product, architecture, data, and engineering teams. Contribute to cloud-native and event-driven architectures, production support, incident management, technical reviews, automation, and continuous improvement. Mentor engineers through code reviews and technical guidance while delivering secure, high-performing business and consumer-facing applications.

Design, develop, and operate scalable backend APIs, microservices, and distributed systems. Build cloud-native, event-driven, data-intensive applications using databases, caching, messaging, search, and observability technologies. Lead technical design, improve performance and reliability, participate in architecture reviews, support production incidents, mentor engineers, and collaborate with product, architecture, data, and engineering teams.

Build and operate Airtasker’s data platform, including dbt models, Python and SQL pipelines, AWS infrastructure, Terraform, Dagster orchestration, ingestion connectors, and self-service analytics datasets. Improve data quality, testing, monitoring, CI/CD, documentation, and platform reliability while collaborating with analysts, data scientists, engineers, and business stakeholders. Support production machine learning use cases and automate workflows across the data lifecycle.