Top Python Jobs in Toronto

Owns end-to-end development of commercial thermal and fluid simulation software, including a C# Windows desktop application, native C++ solver integration, UX, visualization, packaging, and installers. Subsequent work may include Python and C++ numerical solvers, wxPython, VTK, native extensions, geometry and meshing, build and release engineering, web tooling, and porting the product to Linux. The role requires broad technical judgment, multi-language fluency, legacy-code expertise, and practical AI-assisted development experience.

Build and maintain Supabase’s public Python SDK across database, authentication, storage, realtime, and edge functions. Develop natural sync and async APIs, manage typing, packaging, testing, release automation, documentation, and issue triage. Collaborate with cross-functional teams and open-source contributors, support multiple Python environments, maintain WebSocket functionality, and design migration paths for API changes. Work autonomously in a remote, asynchronous environment while making the SDK accessible to developers and AI coding agents.

Owns Fabric Health’s marketing data and instrumentation infrastructure across Segment, React/Next.js, Django, HubSpot, and vendor integrations. Responsibilities include event governance, monitoring, consent propagation, PHI/PII protection, audit logging, compliance controls, growth experimentation, marketing-site development, and campaign instrumentation. The role partners with Engineering, Marketing, Analytics, Legal, and Security while using AI coding tools to build and test reliable integrations.

Operate and improve production cloud infrastructure across Azure and AWS. Implement cloud security controls, vulnerability remediation, monitoring, incident response, and SOC 2 compliance processes. Automate operations with scripting, Terraform or Bicep, and CI/CD pipelines. Support reliability, cost optimization, infrastructure integrations, documentation, and on-call resolution for mission-critical SaaS products.

Lead security incident investigations, serve as Incident Commander, and improve response capabilities through automation and AI agents. Build Python orchestration pipelines, detections, threat signatures, and security guardrails. Investigate modern and AI-driven attacks across cloud, endpoints, identity, SaaS, and containers; perform malware analysis; collaborate with Legal, HR, Cloud, Data, and Product Security; and participate in on-call rotations.

Leads the design and evolution of scalable statistical computing platforms, scientific software systems, automation workflows, reusable coding tools, and data visualization capabilities. Provides technical strategy across Product Development Data Sciences, advises senior stakeholders, leads complex global initiatives, influences standards and governance, and mentors teams. The role requires deep expertise in data science, statistical computing, clinical research environments, regulatory considerations, Python, R, and enterprise-scale platform development.

Owns end-to-end test strategy, designs scalable backend and UI automation frameworks, integrates automated testing into CI/CD pipelines, performs exploratory testing, and advocates for testability and reliability across engineering and product. The role requires strong programming, Kubernetes and distributed-systems knowledge, debugging ability, and cross-functional ownership in a fast-moving startup.

Build trusted finance data products, scalable pipelines, analytical models, automated reconciliations, dashboards, and finance data lake capabilities. Partner with Finance, Accounting, Data Engineering, Product, and Platform teams to resolve discrepancies, improve reporting, automate close activities, establish data quality controls, and enable near-real-time insights. Evaluate AI-assisted automation and own production reliability, testing, documentation, monitoring, and exception handling.

Lead the design and delivery of SaaS products powered by data, ML, and AI, collaborating across backend, systems, and cloud infrastructure. Drive architecture evolution, ensure operability and performance, and mentor engineering teams.

Own the roadmap and backlog for managed investment calculations. Translate investment methodologies and business needs into calculation specifications, requirements, user stories, and acceptance criteria. Analyze complex datasets with SQL, prototype and validate calculations, investigate discrepancies, and collaborate with research, engineering, QA, and stakeholders to ensure accurate production implementation. Prioritize enhancements, manage methodology changes, document calculations, monitor quality and adoption, and communicate delivery risks.

Lead cloud security architecture across AWS and GCP, strengthening identity, access management, workload protection, encryption, secrets management, and secure deployment paths. Build Terraform and policy-as-code guardrails, improve cloud threat visibility and detection, support threat hunting and incident response, and drive strategic security initiatives across engineering teams. Provide technical leadership, mentorship, and guidance while delivering measurable security improvements at scale.

Lead the architecture, design, and hands-on development of enterprise-scale cloud-native applications, data platforms, distributed systems, microservices, and event-driven solutions. Define technical strategy, establish engineering standards, conduct architecture reviews, and guide multiple engineering teams. Build solutions using Java, Scala, Azure, ADLS, Databricks, Kafka, and messaging platforms while mentoring engineers and driving DevOps, CI/CD, scalability, security, and operational excellence.

Designs and operates cloud infrastructure, Kubernetes clusters, networking, CI/CD, and observability systems. Improves developer experience through paved-path tooling, leads production incident triage, investigates issues using logs and scripts, and advances scalable infrastructure architecture. Partners with engineering teams on infrastructure decisions, applies AI tools effectively, and communicates technical trade-offs to technical and non-technical stakeholders.

Develop executive dashboards, scorecards, and governance reporting for the bank’s KYC program. Analyze customer risk, control performance, operational trends, and emerging risks using large datasets. Provide strategic insights, segmentation, recommendations, and data-driven support for regulatory remediation and continuous improvement. Partner with Compliance, Technology, Data Management, Operations, and business stakeholders while promoting data quality, governance, reporting automation, and clear communication to senior leadership.

Own the end-to-end software testing lifecycle across web, mobile, backend, embedded firmware, camera hardware, and sensor systems. Plan and execute manual, exploratory, integration, regression, and release testing; develop Python automation; maintain test environments and coverage; review developer tests; refine acceptance criteria; document defects; and provide quality metrics and go/no-go release recommendations.

Designs, provisions, automates, secures, and manages AWS and Azure cloud infrastructure supporting multiple SaaS product lines. Leads infrastructure projects, builds deployment frameworks, manages cloud costs and access controls, ensures compliance, collaborates with development, SRE, and security teams, documents systems, and participates in on-call rotations.

Supports TD Securities’ Product Control function by overseeing financial controls, P&L, balance sheet, attribution, independent pricing verification, and valuation adjustment reporting. The role validates trading portfolio values, investigates results, provides financial analysis and commentary, integrates new products, improves processes, and partners with trading, technology, accounting, and finance stakeholders to ensure accurate and timely financial reporting.

Develop and deploy production machine learning models for personal banking applications, including marketing, risk management, and strategy optimization. Analyze petabyte-scale structured and unstructured data, engineer features, apply deep learning and LLM techniques, and communicate findings to technical and nontechnical stakeholders. The role also involves documentation, regulatory alignment, risk management, governance remediation, and cross-functional collaboration.

Lead architecture and delivery of large-scale Azure solutions. Design IaC (Terraform) modules and landing zones, CI/CD pipelines, security and governance (PaC, RBAC), observability, cost optimization, and container infrastructure. Mentor engineers, run design/code reviews, collaborate with stakeholders, and drive operational excellence and incident response for enterprise cloud platforms.

Measures and evaluates digital media campaign performance using reporting, statistical analysis, experimentation, and causal methodologies. Builds dashboards, develops KPI frameworks and measurement plans, validates multi-platform data, monitors data quality, and translates analytical findings into actionable recommendations for clients and internal stakeholders.

Own end-to-end analytics engineering for AI and developer experience data. Instrument telemetry, build governed production pipelines, define trusted metrics, and create executive-facing dashboards and analyses. Partner with data engineering, AI, and data science teams to improve data quality and connect raw signals to decision-making. Present clear recommendations on AI investment, developer productivity, and ROI while using AI coding agents to build and maintain pipelines, dashboards, and analyses.

Design, build, and scale backend data services, APIs, microservices, distributed systems, and high-volume data pipelines. Optimize PostgreSQL and Oracle databases, improve reliability and performance across regions, diagnose complex production issues, and mentor engineers. Partner with DBA and SRE teams while providing hands-on technical leadership and owning services end to end.

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.

Measures and evaluates media campaign performance across digital platforms, develops KPI frameworks and dashboards, conducts incrementality testing and statistical analyses, and delivers actionable insights to clients and internal stakeholders. The role also supports data integrations, reporting infrastructure, data quality, and automation while translating complex analytical findings into business recommendations.

Manages data governance and data management practices across sourcing, integration, quality, metadata, lineage, classification, and stewardship. The role maintains data models and documentation, monitors data integrity, supports data profiling and discovery, and coordinates integrations between IT, business data owners, stewards, and subject matter experts. It also identifies process improvements and promotes standards, policies, procedures, and controls for trusted, accessible corporate data.