Top AWS Jobs in Vancouver

Build and operate backend systems supporting post-transaction card account accuracy, issue resolution, and customer communications. Break down projects, deliver work in phases, collaborate with product and cross-functional teams, monitor system metrics, support availability and on-call operations, and contribute to code reviews and interviews. The role requires backend development experience, distributed systems knowledge, and proficiency in Python or Kotlin, AWS, MySQL, and Kubernetes.

Leads and mentors an 8–10-person software engineering team in Vancouver. Oversees architecture, code quality, technical roadmaps, Agile delivery, development toolchain modernization, and technical debt reduction for complex C++ and C# applications. Participates in hiring, onboarding, architectural discussions, code reviews, and cross-functional stakeholder communication. The role supports large-scale APIs, media streaming, hardware integrations, CI/CD, test automation, and cloud or on-premise systems.

Leads technical cybersecurity risk assessments, security architecture reviews, threat modeling, detection engineering, IAM security, incident response, vulnerability management, and threat intelligence. The role evaluates control effectiveness, develops SIEM and EDR detections, strengthens identity protections, supports complex investigations, and drives remediation. It also provides technical leadership, mentorship, governance support, audit assistance, and continuous security improvement across systems, infrastructure, applications, cloud environments, and research projects.

Collaborate with clients to assess business and technical needs, design comprehensive solutions, translate requirements into technical visions, create technical documentation and statements of work, and lead solution validation, delivery, and deployment. Mentor remote teams, communicate technical concepts to diverse stakeholders, and apply cloud, software development, AI, and emerging technologies to improve client outcomes.

Design, build, operate, and improve secure, scalable cloud platforms primarily on AWS. Responsibilities include Terraform-based infrastructure, CI/CD, Docker and Kubernetes environments, Linux administration, networking, IAM, observability, incident response, troubleshooting, cloud migrations, and platform architecture. The role also supports analytics and AI infrastructure, works directly with clients on technical solutions, and contributes to architecture reviews and engineering standards. The position is fully on-site in Vancouver.

Designs, develops, tests, and maintains cloud-based data pipelines and warehouses for large-scale datasets. The role involves data ingestion using Python and SQL, GCP services, production migrations, infrastructure troubleshooting, and optimization. The engineer collaborates with stakeholders to understand project goals and applies AI/LLM tools thoughtfully within data engineering workflows. Responsibilities also include distributed data manipulation, technical guidance, and maintaining scalable, cost-efficient data architecture.

Build and evolve Nova, a distributed OLAP query engine powering Amplitude’s analytics platform. Responsibilities include query planning and execution, columnar storage, caching, distributed compute, JVM performance optimization, cloud cost reduction, observability, reliability, capacity planning, incident response, and operational tooling. The role leads multi-month technical projects, influences architecture, collaborates cross-functionally, and mentors engineers while supporting high-throughput automated query workloads at scale.

Build and maintain scalable, high-performance full-stack applications for an HR, payroll, and hiring platform. Collaborate with product managers, engineering leaders, and cross-functional teams to deliver product features. Apply test-driven development, design distributed systems, work with SQL and NoSQL databases, implement caching, use cloud platforms, and contribute to continuous technical improvement.

Lead a team of data scientists and ML engineers to build and deploy deep learning models for real-time sorting and process decisions from high-dimensional sensor and imaging data. Own architecture, model development (classification, segmentation, anomaly detection, representation learning), data pipelines, productionization on AWS, and mentor the team while collaborating with Engineering, R&D, and Production.

Build end-to-end ML systems for spectral, hyperspectral, and imaging data: ingest and calibrate sensor data, extract features, develop classification/segmentation/anomaly detection and spectral unmixing models, deploy models to production and edge, establish calibration and data quality standards, and collaborate with process teams on experimental design and model evaluation.

Lead development of scalable AI platforms and production machine learning services. Design agentic AI capabilities including tool calling, multi-agent systems, planning, reasoning, and human-in-the-loop workflows. Build Python services and Kubernetes-based ML infrastructure, multi-tenant runtimes, data pipelines, and cloud deployments. Apply MLOps, automation, observability, CI/CD, testing, and distributed-systems practices while collaborating with engineers, researchers, and cross-functional teams.

Build and improve enterprise data products across Match Group brands. Responsibilities include developing reliable ETL pipelines, data models, lakehouse solutions, and scalable data infrastructure; supporting on-premises systems and cloud expansion; translating stakeholder needs into deliverables; defining metrics and data views; maintaining data documentation; training analysts; and advocating for data quality, privacy, performance, and platform improvements.

Build and maintain secure, scalable data pipelines and platform components across AWS and Snowflake. Modernize batch and streaming data flows, support self-serve analytics, and improve testing, observability, security, and CI/CD. Integrate AI capabilities such as anomaly detection and automated tagging. Partner with Finance, Risk, Product, Analytics, ML, and Engineering teams to deliver governed, reliable data while contributing to architecture, design reviews, documentation, and end-to-end project delivery.

Designs, develops, operates, and continuously improves scalable AWS cloud applications, tools, services, and infrastructure. Owns service lifecycles, production troubleshooting, reliability improvements, incident response, and escalated issue resolution. Partners with technology and project teams as a systems subject matter expert while promoting effective operating practices and new technologies. Requires expertise in AWS, cloud infrastructure, automation, infrastructure as code, networking, monitoring, and hybrid connectivity.

Owns GitLab’s enterprise pricing, packaging, monetization, and commercialization strategy. Develops pricing programs, enterprise agreements, bundling, channel and cloud marketplace structures, discounting frameworks, and competitive deal plays. Partners with Product, Sales, Finance, Marketing, Deal Desk, Revenue Operations, and partners to implement scalable commercial programs. Creates pricing enablement materials, makes data-backed recommendations, influences senior stakeholders, and documents decisions in an asynchronous, globally distributed environment.

Build and operate GRC data pipelines, integrations, policy-as-code, continuous control monitoring, evidence automation, and agentic AI workflows. Normalize security data from diverse systems, create auditable evaluation harnesses, govern AI systems, and define technical direction across teams. Partner with security governance, compliance, and engineering groups to transform manual governance processes into scalable products.

Build customer-embedded solutions for Origin’s AI observability platform. Investigate customer traces and operational data using SQL, ClickHouse, APIs, and MCP tools; develop dashboards, integrations, workflows, prototypes, and production code; lead technical discovery; and translate field insights into reusable product capabilities. The role requires close collaboration with enterprise engineers, security teams, AI platform teams, and executives, with travel of up to approximately 20%.

Build and operate Origin’s AI observability platform across data-collection agents, backend services, infrastructure, inference pipelines, APIs, and React interfaces. Design distributed systems using AWS, EKS, ClickHouse, and Kafka for high-throughput, multi-tenant production workloads. Develop internal coding agents and define AI-native engineering practices while making decisions around storage, APIs, performance, and reliability.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Integrate security into software delivery and infrastructure workflows across AWS and GCP. Build scanning into CI/CD pipelines, maintain security guardrails, secure containerized workloads, implement least-privilege access and secrets management, and support compliance with SC TRM and BNM RMiT. Improve monitoring, vulnerability reporting, incident readiness, and secure delivery practices while collaborating with platform and engineering teams using multiple programming languages.

Designs and manages scalable data integration pipelines, governance controls, semantic enrichment, and orchestration systems for enterprise AI and analytics. The role enables LLM, RAG, embedding, and multi-agent workflows; implements security, privacy, compliance, observability, auditability, guardrails, and monitoring for autonomous AI systems. Responsibilities include building connectors and APIs, detecting drift and hallucinations, documenting reusable integration patterns, and collaborating with AI engineers, governance teams, and business stakeholders.

Owns partner and internal sales enablement across channel, hyperscaler, systems integrator, and technology ISV ecosystems. Designs training, certification, GTM content, co-sell plays, account-mapping frameworks, and partner portal programs. Enables field teams and partners around Docker’s AI Governance motion, tracks readiness and pipeline KPIs, and connects enablement investments to revenue outcomes.

Own product engineering work end to end, from understanding customer problems and shaping solutions through implementation, shipping, and iteration. Build and maintain Ruby on Rails, Vue.js, GraphQL, and MySQL systems; develop APIs, improve database performance, prototype architectures, and participate in on-call support. Use and improve coding-agent workflows, contribute to an internal software factory, collaborate with product and design, mentor teammates, and promote engineering best practices.

Leads HackerOne’s partnership sales strategy, revenue performance, and team of seven Channel Account Managers. Builds and scales reseller, technology alliance, and cloud marketplace partnerships, including AWS. Establishes operating models, metrics, forecasting, enablement, and accountability while developing executive partner relationships. Collaborates with Sales, Marketing, Revenue Operations, Legal, Customer Success, and Product to drive partner-sourced and partner-influenced pipeline, revenue, and durable growth.

Build and maintain secure, scalable APIs and infrastructure for an AI-powered fintech platform. Develop features across the stack, integrate third-party APIs, deploy infrastructure with Terraform, apply compliance and security practices, and support customers as a technical point of contact. The role involves close collaboration with the CTO in a greenfield, early-stage startup environment.