Top Data Jobs in Toronto

The Product Manager will lead the identity platform, focusing on improving data quality, performance, and business outcomes through cross-functional collaboration and effective communication.

Lead visual storytelling and design for MEI publications, presentations, interactive dashboards, and web pages. Translate complex economic data into clear visuals, maintain brand consistency, drive adoption of AI tools, collaborate with cross-functional global teams, and deliver production-ready assets across reports, HTML/CSS dashboards, infographics, motion graphics, and client-facing collateral.

Build and operate high-volume event streaming pipelines and analytical databases. Write production Elixir/Python services, integrate legacy pipelines, enforce event schemas and data quality, and provide monitoring, alerting, and SLA-driven observability for event data products.

Design, build, and operate a unified data platform: ingesting, processing, and exposing large volumes of data. Develop scalable ingestion pipelines, storage layers, observability, SLIs/SLOs, multi-tenant models, and metadata/ governance to enable secure cross-product data access and migrations from legacy systems.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, run causal and bandit experiments, decompose merchant economics, and develop agentic deal tooling. Own end-to-end data pipelines, ETL, monitoring, and visualization. Evaluate AI systems for accuracy, bias, and drift, and partner with cross-functional teams to translate results into pricing strategy and automation.

The Staff Data Analyst will lead data strategy for Compliance, analyzing regulatory engagements, and driving effectiveness through actionable insights and collaboration across various teams.

Lead design and operation of Block's real-time, event-driven data systems. Build and run high-throughput streaming pipelines, turn Kafka event streams into low-latency data products, improve reliability and observability, mentor engineers, and define platform architecture and standards for projections, third-party data proxy, and graph data capabilities.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Build and productionize statistical and ML models for Toast's restaurant platform. Partner with engineers, product, and business teams to mine data, design experiments, evaluate models, and communicate actionable insights that drive product growth and customer outcomes.

Lead industry-focused market strategy and growth for Manufacturing by engaging executives, shaping go-to-market plays, accelerating commercial opportunities, and enabling sales and delivery teams. Drive thought leadership, create repeatable industry assets, develop business cases and transformation roadmaps, and represent the company at events to position Microsoft-based Cloud, Data, and AI solutions for measurable business outcomes.

Drive industry-led growth for CPG and HLS/Professional Services by translating market trends into scalable Microsoft-based solutions. Engage executives, support strategic pursuits, build go-to-market plays, produce thought leadership, and enable sales, delivery, and marketing teams to accelerate customer transformation and commercial outcomes.

Lead industry strategy and market growth for Insurance by identifying trends, shaping go-to-market plays, engaging executives, accelerating commercial opportunities, and enabling sales, delivery, and marketing through Microsoft Cloud, Business Applications, Data, and AI solutions.

Build and operate ML training and serving infrastructure: design, develop, and launch backend systems, support availability and on-call, collaborate cross-functionally, and improve developer velocity.

Lead technical implementation of a market management platform for nodal electricity markets. Design and operate large-scale, data-intensive production systems handling continuously changing data. Make architecture decisions, contribute production software, manage incidents, improve existing systems, and partner with product, engineering, data science, and power-market experts. Lead technical reviews, mentor engineers, and drive optimization, forecasting, and analytical capabilities.

The DCIM Administrator manages data center operations by overseeing Nlyte workflows, ensuring accuracy in asset management, and driving continuous improvement in process and data integrity.

Lead development and execution of enterprise data controls and governance. Assess controls, close gaps, implement data quality checks and monitoring, define standards and documentation, support platform migrations and AI initiatives, partner with cross-functional teams, and manage/coaching a team of data analysts to ensure regulatory-compliant, high-quality data.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, forecasting, targeting, and personalization. Collaborate with engineers and product teams, set modeling and productionization standards, mentor data scientists, guide architecture and experimentation, and apply cloud and MLOps best practices to drive measurable product and business impact.

Lead end-to-end data science initiatives: design, deploy, and monitor ML/statistical models for credit risk, pricing, collections and fraud. Collaborate cross-functionally to integrate models into production, define model development standards, mentor team members, and maintain scalable ML pipelines and monitoring systems to drive measurable business impact.

Lead full-cycle technical recruiting for Data/ML, Platform, and Engineering; build predictable pipelines, design structured interview processes and scorecards, run high-signal screening, partner with senior leadership, and improve recruiting workflows to hire and retain high-performing talent.

Analyzes pipeline, funnel conversion, campaign performance, retention, and revenue drivers across Sales, Marketing, and Client Success. Builds and maintains dbt models, data marts, quality controls, documentation, and BI-as-code assets using Salesforce and modern data-stack technologies. Partners with stakeholders to turn ambiguous questions into decisions and advances AI-agent workflows for faster analytics and reporting.

Own Coupa’s AI data governance and readiness strategy, including data fitness standards, AI asset inventories, quality monitoring, source-of-truth definitions, and governance policies. Partner with AI engineering and data teams on API gateway controls, PII protection, prompt security, observability, compliance, and incident triage. Translate GDPR, SOC 2, EU AI Act, NIST AI RMF, and related requirements into actionable standards while supporting enterprise AI/ML delivery.

Design, build, and maintain SparkSQL/PySpark data pipelines in the central data lake to ingest IoT, product, and unstructured data (video/audio). Produce reliable computed tables for analytics, model training, and dashboards; integrate external datasets; ensure high data quality and uptime; and collaborate with Data Science, ML, and cross-functional teams.

Design, build, and operate scalable data ingestion, replication, and lakehouse infrastructure to move petabytes of data into a Delta Lake on S3. Improve reliability, observability, security, and developer experience for Spark/Databricks processing. Develop internal libraries and tooling (Go/Python), collaborate with cross-functional teams, and help shape long-term data platform and AI-ready infrastructure.

Design, build, and operate large-scale Spark/PySpark data pipelines and the enterprise data platform. Implement data integrations, transformations (Python/SQL), monitoring, and data quality. Build MCP servers and AI-enabled tooling, optimize performance, mentor engineers, and lead cross-functional data projects to enable analytics and automation.