Top Data Jobs in Toronto

Conducts clinical data analysis, audit coordination, regulatory reporting, workflow documentation, and corrective action planning for clinical operations. Reviews HEDIS, URAC, and NCQA compliance data, develops performance metrics and reports, supports audits and staffing analysis, researches healthcare regulations, and recommends process improvements. The role also assists with clinical staff training, business requirements, operational documentation, and supervisory coverage.

Develop quantitative models, statistical analyses, experimentation frameworks, and scalable data pipelines to solve product and business challenges. Partner cross-functionally with product, engineering, design, research, and analytics teams to generate actionable insights, improve products, and establish scientific best practices. Apply AI to accelerate workflows while validating AI-assisted outputs and protecting sensitive data.

Analyze spatial and product data to improve Lyft’s mapping, routing, and travel-time estimation products. The role designs online experiments, develops performance metrics and analytical frameworks, identifies growth and efficiency opportunities, and translates insights into product and business decisions. The Data Scientist partners with engineers, product managers, and operators while communicating recommendations to senior stakeholders. Responsibilities also include monitoring rider and driver experience and applying statistical analysis, programming, visualization, and machine learning techniques.

Design, build, deploy, and optimize high-performance distributed data platform services supporting analytics and machine learning. Responsibilities include developing scalable streaming infrastructure, participating in design and code reviews, tuning performance, debugging production issues, collaborating cross-functionally, and mentoring engineers. The role requires expertise in distributed computing, messaging, data processing, cloud technologies, software engineering principles, and reliability practices.

Design, build, and operate scalable data platforms covering ingestion, orchestration, modeling, quality, observability, governance, and BI enablement. Develop batch and streaming pipelines with Airflow, Snowflake, dbt, Terraform, and CI/CD tooling. Establish engineering standards, data access controls, and AI-assisted workflows while enabling software, analytics, and non-technical users to build production-grade data products. Provide technical leadership, vendor management, documentation, training, and full DevOps ownership.

Owns production machine learning systems and automated decisioning, including graph-based models, RAG systems, real-time serving, monitoring, evaluation, drift detection, and retraining. The role establishes ML engineering and MLOps standards, shapes platform strategy, mentors scientists, and partners with senior leaders. Responsibilities include developing SignalGraph, improving LLM evaluation, applying modern deep learning methods, and communicating technical decisions and uncertainty to cross-functional stakeholders.

Develop programmatic advertising and data strategies to support sales, client solutions, and revenue growth. Advise sales teams on programmatic deals, data segments, campaign optimization, and client presentations. Use LLMs and generative AI to automate workflows, analyze datasets, and produce insights. Maintain monetization dashboards, support budgeting and forecasting, research new opportunities, and build strong client and partner relationships.

Develops and supports data and AI products, translating business requirements into technical solutions. Responsibilities include data pipeline and warehouse development, testing, documentation, defect resolution, technical design, automation, reusable framework creation, process improvement, and stakeholder collaboration. The role requires ETL/ELT, SQL, NoSQL, Python, Spark, Scala, cloud, Hadoop, data profiling, batch and streaming data, and emerging AI capabilities.

Develop and maintain scalable Java and big data applications across the software development lifecycle. Build distributed data-processing tools, REST APIs, and automation; analyze and debug systems; conduct test-driven validation; review code; and collaborate with internal teams and vendors. The role works with technologies including Apache Hudi, Trino, Spark, Kafka, cloud storage, and distributed computing platforms while promoting performance, reliability, modern engineering practices, and continuous technical improvement.

Leads mechanical engineering design and delivery for hyperscale and mission-critical data centers. Responsibilities include designing cooling, HVAC, plumbing, and fire protection systems; coordinating multidisciplinary teams; preparing technical documentation; reviewing deliverables; supporting construction and commissioning; ensuring code compliance; optimizing energy performance; advising clients; contributing to business development; and mentoring engineers. The role is remote anywhere in Canada with periodic travel for client meetings, site visits, commissioning, and project collaboration.

Develop machine learning and statistical models, analyze data, run experiments, and apply causal inference and optimization to improve Stripe’s products, risk, finance, growth, and go-to-market decisions. Partner with cross-functional teams, translate complex analyses into actionable recommendations, manage multiple projects, and help deploy and optimize production models.

Leads partner-led and enterprise sales growth, primarily through AWS and Microsoft ecosystems. Builds strategic partnerships, develops enterprise accounts, drives end-to-end sales cycles, expands SMB sales processes, positions cloud and digital engineering capabilities, and manages pipeline forecasting. The role also helps shape sales strategy, organization, and scalable go-to-market processes.

Owns biological knowledge representation through LinkML and Biolink ontology engineering, semantic mappings, metadata harmonization, and schema governance. The role administers agentic curation and MCP tooling, develops LLM-driven extraction and validation workflows, and establishes automated quality controls, provenance, CI/CD, and evaluation standards. This senior individual contributor translates biological research requirements into canonical data models supporting genomics, single-cell, screening, imaging, and prior-knowledge datasets.

Own and scale a multi-cloud data platform across AWS and Azure. Manage Apache Airflow, Kubernetes, Terraform, Helm, ArgoCD, CI/CD, observability, big data infrastructure, governance tooling, and FinOps initiatives. Provide end-to-end architectural and operational ownership, improve developer tooling, define platform roadmaps, manage incidents, enforce engineering standards, and mentor data platform engineers.

Develop and deploy production-grade AI, machine learning, and generative AI solutions for insurance business challenges. Responsibilities include building LLM applications, prompt engineering, RAG workflows, data pipelines, experiments, proof-of-concepts, and scalable AI systems. The role involves monitoring model performance, generating business insights, collaborating with stakeholders, and communicating technical findings to diverse audiences.

Lead data modeling and architecture for complex pension administration and analytics systems. Design logical and physical data models, data flows, migrations, mappings, transformations, schemas, and architectural documentation. Establish standards for metadata, security, governance, quality, classification, and data integrity. Collaborate with business, engineering, DevOps, infrastructure, security, and architecture teams to deliver database solutions and coordinate schema changes across environments.

Develops and maintains financial and business reports, dashboards, and client reporting packages. Prepares, reconciles, and validates data from complex accounting systems, interprets trends and risks, and translates stakeholder requirements into reporting deliverables. Provides analytical support to lawyers, business services professionals, clients, and firm initiatives while documenting processes, reviewing other analysts’ work, and supporting reporting improvements using Excel, Power BI, SQL, and related tools.

Own MediCore’s end-to-end data architecture, including operational databases, analytics platforms, streaming pipelines, governance, security, and AI/ML data foundations. Design healthcare data integrations using HL7, FHIR, EHR, and third-party APIs; establish multi-tenant data isolation and residency strategies; ensure HIPAA, PHIPA, and SOC 2 compliance; guide reporting, migrations, engineering standards, and technical teams across multiple countries.

Lead Pebl’s data strategy, infrastructure, governance, analytics, and AI integration. Own scalable data engineering pipelines and secure global employment data while translating insights into business outcomes across Marketing, Sales, Operations, Finance, and Legal. Build AI capabilities for products and internal workflows, communicate data-driven narratives to executives and the board, and recruit and develop a high-performing team of data engineers, analysts, and AI specialists.

Designs, builds, and optimizes scalable data solutions for omnichannel marketing, analytics, and AI products. Responsibilities include developing Python and SQL ETL pipelines, enterprise data warehouses, BI dashboards, system integrations, data models, and AI-enabled solutions. The role gathers requirements from clients and stakeholders, supports Snowflake-based platforms, improves data quality and performance, and establishes reusable engineering practices, documentation, and Git-based workflows.

Lead architecture and hands-on engineering of Pantheons data platform: extend ingest, curation, and retention services; build ML/LLM-ready data infrastructure; mentor engineers; operate in a DevOps model; ensure data governance, observability, reliability, and cross-functional partnership.

Own revenue-focused data science from opportunity sizing through production ML. Investigate and measure product changes, build and operate batch and online ML systems for personalization, experimentation, and automation, and partner cross-functionally to integrate models into user-facing products. Establish monitoring, lineage, reproducibility, and security practices; improve data pipelines; mentor data scientists; and define ML development best practices while using AI tools to accelerate delivery.

Own the strategy, roadmap, and delivery of Coalition’s scanning and cyber-risk data products. Partner with Engineering, Data, Security, Underwriting, Actuarial, Design, and customer teams to improve asset discovery, data quality, accuracy, reliability, scalability, and usability. Define metrics, prioritize initiatives, translate technical challenges into product requirements, guide discovery through launch, and develop APIs and experiences that help stakeholders understand and act on cyber-risk intelligence.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Own end-to-end financial data analysis, including SQL and dbt data modeling, Looker dashboard development, data quality validation, reconciliation, and stakeholder-facing insights. Partner with Finance and Payments to support reporting, planning, revenue analysis, cost insights, and transaction-level analytics. Translate ambiguous business needs into reliable technical solutions while improving documentation, QA, modeling, and data architecture practices.