Top Data Jobs in Toronto

Build, deploy, and maintain machine learning models on large-scale datasets. Extract, clean, and analyze numerical and textual data. Develop model pipelines, run experiments, optimize business strategies, and evaluate new data sources and technologies.

Manage moderately complex technical projects and small programs across multiple teams: create and maintain plans, track risks/dependencies, align stakeholders, apply Agile practices, report progress, and drive delivery and continuous improvement.

The Engineering Manager leads Data Engineering teams, developing scalable data solutions and collaborating with stakeholders to drive McCain's digital transformation.

Lead design and delivery of enterprise-scale AI/ML solutions for Ethoca, building and governing models (NLP, generative, multi-modal) to reduce chargebacks, improve dispute resolution, and scale production systems across cloud environments.

Lead development, deployment, and monitoring of end-to-end ML/DL/AI solutions. Responsibilities include data extraction, cleaning, modeling, evaluation, production deployment, documentation, and presenting results. Collaborate across teams, build prototypes and POCs, protect IP, and disseminate knowledge externally.

Lead product marketing for Procore AI & Data products: own positioning, messaging, launches, competitive positioning, sales enablement, and cross-functional GTM programs. Collaborate with Product, Demand Gen, Sales, and global Marketing to create customer-facing content and drive adoption.

The Enterprise Architect leads the development of AI and Data architecture, aligning technology with business needs while ensuring adherence to governance standards. They provide strategic guidance, collaborate with stakeholders, and utilize advanced technologies to enhance solutions and processes.

Lead a small team and contribute hands-on to design, develop, deploy, and iterate AML and crypto risk detection machine learning models. Own technical roadmap, collaborate with Product and Engineering, ensure model quality, explainability, documentation, and governance, and drive production performance and outcomes across the model lifecycle.

Lead marketing analytics and data strategy for B2B SaaS Marketing. Design measurement frameworks, own attribution, funnel and lifecycle analysis, forecasting, and marketing investment recommendations. Partner with senior marketing leaders and cross-functional teams to build data foundations, enable AI-assisted analytics, and mentor analysts to raise analytical standards.

Lead client-facing data solutioning and pre-sales within Consumer verticals, design and translate architectures into delivery, maintain ~50% billable involvement, drive pipeline growth and close complex data opportunities while ensuring information security and privacy compliance.

CommBank is seeking Indigenous job seekers for various roles across its sectors, offering traineeships, internships, and direct employment opportunities with support from an Indigenous Careers team.

The GTM Lead for Insurance will build and manage enterprise insurance accounts, develop relationships with senior industry leaders, and shape sales strategies for AI solutions.

The Data Architect will define and govern Global Transaction Banking’s enterprise data platform architecture, including ingestion, data products, APIs, streaming, governance, quality, observability, and AI/ML foundations. The role establishes engineering standards, leads architecture reviews, ensures regulatory and enterprise compliance, guides platform scalability and resilience, and mentors senior engineers. This hands-on position requires deep Databricks and Spark expertise, event-driven architecture experience, Azure delivery, and leadership in production-scale data platform design.

Lead PICTON’s enterprise data strategy, architecture, governance, and engineering capabilities across investment data, analytics, and AI. Build scalable Databricks and Snowflake platforms, reliable data pipelines, financial data models, semantic layers, APIs, and governance standards. Partner with Engineering, Investment, Product, and AI leaders while building and managing a high-performing data platform team. Translate complex financial data into reliable products that support portfolio construction, risk, performance, analytics, applications, and AI workloads.

Leads the strategy, architecture, engineering roadmap, and operational excellence of TD’s Databricks-powered Global Transaction Banking data platform. Oversees distributed engineering teams building data products, APIs, streaming services, cloud applications, analytics, and AI capabilities. Establishes architecture, governance, security, reliability, DevSecOps, and delivery standards while managing budgets, vendors, risks, and transformation initiatives. Develops engineering leadership and technical talent across global teams in Toronto, New York, and Bangalore.

Build and maintain batch and streaming ETL pipelines using Databricks, Apache Spark, Azure, and Delta Lake. Develop CDC and SCD1/SCD2 solutions, data models, federated lakehouse connections, and governed Unity Catalog environments. Configure catalogs, schemas, tables, views, functions, and volumes while optimizing Spark performance through partitioning and liquid clustering. Apply data warehousing, security, CI/CD, and DevOps practices to deliver reliable cloud data platforms.

Own marketing attribution and data enablement for Demand Generation: improve first- and multi-touch models, integrate new sources, write production SQL, maintain seed tables/UTMs, validate data pipelines, enable stakeholders with documentation and training, and support top-of-funnel analytics and prospect data flows.

Own daily MDM operations and data supply, manage data requests via Jira, ensure data integrity and quality, lead root-cause remediation, deliver data requirements for projects, define data standards and controls, drive automation and continuous improvement, and report on operational performance and data health.

Lead design, architecture, and delivery of enterprise-scale data platforms using Databricks and Spark. Architect Lakehouse solutions, build ETL/ELT pipelines, implement IaC and CI/CD, ensure data governance and security, mentor engineers, and partner with Fortune 500 clients from discovery through production.

Design, build, deploy, and own high-performance, scalable data-platform components for streaming analytics, reporting, and ML. Collaborate with cross-functional teams, perform design/code reviews and performance tuning, debug production incidents, participate in on-call rotation, and mentor engineers to scale the organization.

Build, maintain, and optimize data pipelines and transformations on Snowflake and Azure. Develop DBT models, ingest diverse data sources, monitor pipelines, troubleshoot issues, collaborate on governance, and use GenAI-assisted tools to accelerate development and documentation.

Use customer, digital, and transportation data to build targeting, propensity, pricing, loyalty, and churn models; run experiments (A/B), optimize promotions and pricing, present insights to stakeholders, manage data structures in Databricks/AWS, and lead project work with business partners to drive data-driven decisions.

Lead end-to-end development and deployment of AI/ML (including Agentic and LLM) solutions for enterprise-scale data reconciliation. Design models, manage pipelines and MLOps, analyze large structured/unstructured financial datasets, collaborate with engineering and business teams, maintain documentation, and deliver measurable impact across operations, risk, and finance.

Lead enterprise data quality strategy by designing frameworks, controls, and scorecards; integrate controls into data pipelines; use AI/ML and Generative AI for anomaly detection and automation; manage end-to-end data issue lifecycle, perform root-cause remediation, assess control effectiveness, support audits/regulatory reviews, and partner with business, technology, risk, and data teams to improve upstream data quality and governance.

Lead design, development, deployment, and operational support of scalable cloud data platforms and ELT/ETL pipelines on Azure. Build data products for analytics and AI, optimize performance/cost, implement CI/CD and observability, collaborate with stakeholders, and troubleshoot complex data workflows using Snowflake, dbt, Python/PySpark, and orchestration tools.