Top Data Jobs in Toronto

The Director, People Business Partner will enhance organizational effectiveness, talent management, leadership support, and people strategies using AI and data to improve outcomes and performance.

The Senior Marketing Analytics Lead will oversee analytics projects, translate data into insights, influence marketing strategies, and mentor junior team members.

Lead global go-to-market and commercialization for Experience & Disputes products, translating product capabilities into value propositions, driving launches, sales enablement, pipeline growth, market intelligence, and performance optimization across cross-functional stakeholders.

As a Product Manager, you'll lead product vision and strategy, utilizing customer insights and technology to create innovative solutions while collaborating with multiple teams.

Lead global portfolio marketing for Advisors & Transformation, defining positioning and go-to-market for AI-enabled consulting (including Agent Suite). Drive integrated campaigns, sales enablement, thought leadership, and data-driven insights to accelerate adoption, pipeline, and revenue growth while aligning product, consulting, and regional teams.

Lead digital transformation initiatives, enhance commercial strategy, manage technology portfolios, and drive operational excellence in marketing and sales.

Supports data-driven solutions by building Power BI dashboards, automating workflows with Power Automate, developing basic Power Apps, and cleaning and validating large datasets. Performs ad hoc analysis, creates VBA scripts and Excel macros, and communicates findings to support business decisions. The role also provides exposure to AI modeling, prompting, SQL, Power Query, automation, and low-code development.

Lead the architecture and development of scalable data platforms and distributed processing systems. Establish enterprise data governance, quality, privacy, compliance, schema management, and lifecycle standards. Drive resilient, automated, and observable data pipeline strategies; evaluate technologies; review complex architectures; collaborate with business and technology stakeholders; and mentor data engineers in stream processing, data modeling, and MLOps.

Design, build, and maintain scalable data pipelines and storage (data lakes/warehouses). Integrate diverse sources, enforce data quality, security and compliance, optimize ETL/ELT, monitor pipelines, and mentor junior engineers while collaborating with data scientists, analysts, and stakeholders.

Serve as the functional expert for SAP financial data, ensuring data integrity and alignment with P&L/Balance Sheet. Support SAP BW-IP planning, Power BI and BW reporting, troubleshoot complex issues, optimize BW data models, and enable integrations with modern data platforms like Databricks and OneStream.

Lead a team of senior product managers owning data ingestion, normalization, event processing, workflows, third-party integrations, and data governance. Define unified roadmap, partner closely with engineering, manage vendor relationships, launch integrations, and represent platform needs to Sales, Partnerships, and Customer Experience while prioritizing customer outcomes.

Own, build, and optimize Spark-based production data pipelines and ML data lake on GCP Dataproc to support recommender systems. Collaborate with ML engineers, resolve performance bottlenecks, and deliver scalable, reliable data products for AI-driven personalization.

The Senior Data Engineer will design and maintain streaming data pipelines, develop analytical databases, and ensure data system reliability while collaborating with teams across the organization.

Owns a portfolio of customer-domain data products supporting Customer Success, Support, Product, and Sales. Leads data maturity projects, roadmap prioritization, requirements definition, data mart and metric design, quality and governance, privacy, adoption, and stakeholder alignment. The role bridges business and analytics engineering teams, guiding technical delivery and ensuring reliable, discoverable, and valuable data products.

Lead a data science team to build scalable data pipelines, ML models, and tooling on AWS; operationalize and analyze large structured and unstructured datasets; collaborate across teams to source data and optimize business programs; hire and develop talent while evaluating new technologies for digital banking.

Lead the development of AI and machine learning solutions at Mastercard, defining strategy and mentoring teams while ensuring compliance and enhancing consumer experiences.

Design, build, and maintain end-to-end data pipelines in Snowflake using Medallion Architecture. Develop ETL/ingestion with Python and/or Informatica, implement Snowflake AI/ML features, manage version-controlled code (Git/Bitbucket), support CI/CD, monitor and optimize pipelines, and ensure data quality and performance across environments.

The Account Executive will drive revenue by engaging mid-market and enterprise businesses in data-sensitive sectors, managing the sales process, and building relationships with C-level stakeholders.

Lead analytics for Zynga Poker by running A/B tests, building ETL pipelines and dashboards, analyzing player behavior, tuning game economy, and improving fraud/bad-actor models. Collaborate with product, design, and data science teams, contribute to AI/LLM initiatives, and maintain telemetry and BI tools to inform strategic decisions that impact millions of players.

Design and build scalable data systems and pipelines, migrate legacy systems, ensure data governance, and collaborate with cross-functional teams to enhance data accessibility and reliability.

Design and maintain ETL pipelines and Databricks Gold-layer tables; develop semantic layers, data dictionaries, and documentation for AI-driven BI; build dashboards and analyses to surface marketing and sales KPIs; perform ad-hoc deep dives; translate stakeholder requirements into scalable, analytics-ready data models; enable self-serve reporting and agentic AI workflows.

Lead enterprise architecture strategy focused on Data and AI: define standards, future-state capabilities, and capability mapping; analyze systems for modernization; integrate architecture with data governance; guide solution design, select modernization tools, manage technical risks, mentor architecture team, and collaborate on roadmaps, LCM and decommissioning while driving adoption of modern technology stacks.

Validate, reconcile, and analyze large datasets to identify data quality issues. Investigate anomalies, perform root-cause analysis, and recommend solutions. Work with business and technology stakeholders to define and execute validation activities, produce documentation and reports, and improve data quality processes and controls.

As a Senior Associate, Data Analyst at Capital One, you will analyze datasets to support business strategies, communicate insights, and enhance decision-making through data visualization and stakeholder collaboration.

The Product Manager for Data Products at Capital One will drive the design and operationalization of data products, prioritizing long-term roadmaps, defining metrics-driven strategies, and collaborating across teams to enhance customer experiences.