Top AWS Jobs in Toronto

Build and operate Morningstar’s internal developer platform, including deployment automation, platform services, command-line tooling, and AWS infrastructure. Own features through design, development, testing, deployment, and monitoring. Troubleshoot applications, CI/CD pipelines, AWS resources, and Kubernetes workloads while improving reliability, security, scalability, observability, documentation, and developer experience.

Develop data structures and pipelines in data lakes and Snowflake, maintain data quality and processes, build automated reporting and visualizations, and analyze large datasets. Collaborate cross-functionally to identify business problems, recommend solutions, and support deployment while communicating technical concepts to nontechnical audiences.

Data Analytics Engineering Intern supporting cross-functional teams by transforming data into data lake and Snowflake structures, maintaining data processes and pipelines, ensuring data quality and documentation, building automated reports and visualizations, and analyzing large datasets. The role involves solving business problems end to end, from identifying requirements through deploying practical data solutions.

Build and maintain data structures, pipelines, reporting, visualizations, and data processes using data lake and Snowflake technologies. Ensure data quality, documentation, and usability while performing investigative analysis on large datasets. Collaborate cross-functionally to identify business problems, recommend solutions, and support end-to-end deployment. The role requires a quantitative bachelor’s degree, coding experience, SQL or dbt familiarity, and data pipeline experience.

Leads end-to-end technology delivery and transformation across the Wealth Management value chain. Advises CIO, CTO, and COO stakeholders on modernization, architecture, operating models, build-versus-buy decisions, roadmaps, budgets, and vendor governance. Shapes cloud, microservices, event-driven, API-led, data, and DevOps strategies while improving operational efficiency, performance, and advisor and client experiences. Requires extensive Wealth and Asset Management consulting leadership, technical fluency, platform integration experience, and regulatory awareness.

Lead design and build scalable data architectures, ETL pipelines, ingestion and data quality frameworks. Collaborate with cross-functional teams, enforce data governance, analyze data for insights, mentor junior engineers, and serve as the technical authority on data engineering best practices.

Leads architecture and development of BuildOps’ integration and data platform. Designs APIs, service patterns, databases, event-driven systems, and scalable cloud infrastructure while improving observability, reliability, testing, and deployment practices. Drives technical strategy across multiple engineering teams, mentors engineers, evaluates technologies, and collaborates cross-functionally to deliver secure, high-quality platform capabilities and financial data integrations.

Leads end-to-end delivery of complex technical programs across cloud, infrastructure, platform, security, and engineering domains. Owns roadmaps, capacity planning, governance, milestones, dependencies, risks, executive reporting, Jira administration, and release readiness. Partners with Engineering, Product, Architecture, Infrastructure, Security, and Operations teams to align priorities and drive delivery. Improves program management processes and serves as the primary contact for strategic initiatives.

Manage strategic G2000 customer accounts as a technical and business advisor. Drive retention, consumption growth, revenue realization, executive advocacy, and long-term success through customer business reviews, roadmap alignment, forecasting, and risk mitigation. Build relationships with C-suite and technical leaders, coordinate global resources, influence product strategy, and lead complex migrations and modernization initiatives.

Build and scale frontend applications, backend services, APIs, integrations, and production systems. Own projects from technical design through rollout and maintenance, improve reliability, performance, scalability, and observability, and collaborate cross-functionally with Product and Design. Mentor engineers, contribute to architecture and engineering standards, resolve ambiguous technical problems, and use AI-assisted tools to accelerate development while maintaining sound engineering judgment.

Develop and administer Tableau Server, build optimized data sources and dashboards, write and tune SQL, and perform exploratory data analysis. Partner with business, product, and analytics teams to deliver scalable reporting solutions. Establish visualization standards, document analytical processes, and evaluate AI/NLP tools such as Amazon Q to improve self-service analytics and natural-language querying.

Build and operate foundational infrastructure for PagerDuty’s real-time platform, including networking, compute, Kubernetes, and ingress systems. Improve reliability, scalability, and security; monitor system health through metrics, logs, and alerts; participate in 24/7 on-call rotations; support infrastructure rollouts; and contribute to agile planning and technical improvements.

Lead and manage MongoDB’s Storage Execution team, owning the roadmap for durability, availability, performance, transactions, storage architecture, index operations, and developer productivity. Partner with adjacent engineering and cloud teams, manage customer escalations, drive cross-team initiatives, improve engineering processes, and develop engineers through hiring, coaching, and performance management.

Build Python-based frameworks to validate, reconcile, cleanse, and transform large datasets. Use Pandas and NumPy to identify data quality issues, discrepancies, duplicates, and anomalies. Automate validation workflows, investigate root causes, produce data quality reports, optimize processing, and collaborate with business, analytics, and engineering teams on data migration, onboarding, and integration initiatives.

Leads Zynga’s global centralized data platform and product strategy, overseeing real-time ingestion, cloud data warehousing, experimentation, metrics, governance, and AI/ML capabilities. Manages approximately 60 employees across multiple regions, including senior engineering, product, and program leaders. Drives AI adoption, organizational change, platform reliability, data quality, compliance, and self-service analytics while partnering with studio and executive stakeholders.

Architects and develops AI-powered systems, backend services, APIs, experimentation frameworks, and automation using foundational models. Integrates AI capabilities with microservices, AWS, Snowflake, and diverse technology stacks while ensuring scalability, reliability, and maintainability. Collaborates with product, data science, and engineering teams to translate business needs into technical solutions, evaluate emerging AI tools, and rapidly prototype proof-of-concept applications.

Manage a multi-million-dollar book of business, retain and grow high-value customer accounts, build qualified sales pipeline, prospect into technical leaders, and close opportunities. The role requires developing MongoDB product expertise, articulating complex technology value, building customer relationships and champions, and partnering with solution architects and professional services. Success depends on quota attainment, strategic prospecting, sales methodology, and effective territory management.

Build and improve test infrastructure, automation frameworks, CI/CD pipelines, and end-to-end testing strategies. Lead quality initiatives, develop AI-augmented testing solutions, monitor test environments, support incident resolution, and use quality and performance metrics to improve software reliability. Collaborate with Infrastructure, SRE, engineering leaders, and product teams in an agile environment to expand test coverage and enable safe, efficient delivery.

Design, develop, launch, and maintain scalable backend systems and APIs used by partners and merchants. Collaborate with engineering teams and stakeholders, balance delivery speed with system quality and reliability, navigate large codebases, debug software, conduct code reviews, and write clear, tested, extensible code using distributed-systems technologies.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, workflow automation, evaluation, and observability capabilities. Work hands-on with Python, SQL, dbt, lakehouse data, cloud infrastructure, model selection, and inference-cost optimization. Establish reusable engineering patterns and mentor teams through high-quality code and system design.

Build and operate backend infrastructure and APIs for stablecoin-based payments, deposits, withdrawals, and transfers. Own projects from system design through production, improve distributed-system performance and reliability, collaborate with Product, Design, Infrastructure, and Compliance, and contribute to engineering quality through reviews and architectural best practices. The role requires blockchain integration experience and responsible use of generative AI.

Partner with R&D and engineering leaders to manage Cloud, Cellular, and AI infrastructure costs. Analyze financial results, produce management reporting, support month-end close, build forecasts, improve reporting models, develop performance KPIs, and drive annual operating plan planning. The role also involves cost optimization, financial modeling, cross-functional collaboration, and using AI tools to streamline reporting and analysis.

Leads Samsara’s Quality Engineering and Test Infrastructure organization, setting strategy and building scalable testing platforms, automation frameworks, environments, simulators, test data systems, and CI/CD integrations. Establishes quality metrics, improves regression prevention and release confidence, reduces flaky tests and feedback times, and partners across engineering and product teams. The role also includes AI-assisted testing initiatives, exploratory validation, organizational leadership, hiring, coaching, and developing engineering managers and senior individual contributors.

Lead international integration engineering by architecting scalable distributed systems and an AI-powered development harness for partner integrations. Guide technical strategy, mentor engineers, review architecture and code, establish development and deployment best practices, and deliver production solutions. Collaborate with product, design, QA, UX, and partner engineering teams while using Java, Kotlin, event-driven messaging, and cloud technologies.

Lead delivery of major iOS software components and platform capabilities, collaborating with engineers and architects. Design, develop, test, implement, and support technical solutions throughout the software development lifecycle. Improve engineering practices, contribute technical and architectural expertise, and mentor junior, intermediate, and senior mobile engineers. The role requires advanced hands-on development using native iOS technologies, Swift, and SwiftUI, with exposure to cloud and agile environments.