Top Data Jobs in Toronto

Lead design and implementation of scalable Hadoop/Spark big data platforms and pipelines, optimize cluster performance, enforce data security/governance, mentor engineers, and integrate cloud-native services for analytics and near-real-time ingestion.

The Intermediate Data Engineer designs and builds scalable data solutions, manages data ingestion and pipelines, and supports BI initiatives. Responsibilities include collaborating with stakeholders, ensuring data integrity, and mentoring team members while adhering to best practices in data engineering.

The Senior Marketing Data Analyst will analyze global marketing data to provide strategic insights, optimize campaign performance, develop customer segmentation models, ensure data governance, and support reporting through BI tools.

The Senior Programmer Analyst leads application system development, ensuring quality coding and testing. They address complex data integration issues and mentor teams on ETL processes and compliance, focusing on operational excellence and continuous improvement.

The Lead Financial Data Analyst manages financial instrument data collection, quality control, client inquiries, and operational risk compliance, while coaching others and improving processes.

Design and maintain enterprise data architecture and large-scale conceptual, logical, and physical data models for wholesale lending. Define target-state architecture, metadata, data lineage, integrations, and governance. Collaborate with stakeholders, lead junior staff, optimize systems for performance, and support cross-functional adoption and compliance.

The Financial Data Operations Manager leads data operations teams, ensures SLA compliance, manages client relations, and oversees financial instrument data processing and governance.

As a Lead Financial Data Analyst, you will manage the collection, control, analysis, and distribution of financial data for clients, ensure compliance with SLAs, and guide process improvements.

The Senior Data Architect will design data architecture and integration workflows for PM2PA, focusing on optimizing collaboration and ensuring data quality within Oracle EBS and cloud environments.

Lead day-to-day privacy and cross-border data transfer governance, assess and validate controls, support audits and regulatory compliance, manage risk acceptances, coordinate policy implementation, and deliver training to reduce data privacy operational risk across business units.

Lead and/or staff-level technical roles to design, build, and operate petabyte-scale batch and streaming data pipelines, APIs, and analytical platforms. Responsibilities include platform architecture, data modeling, query and API design, reliability, observability, performance optimization, cross-team coordination, and mentoring engineers through a transformation to workload-aware, near real-time data systems.

Lead product-focused analytics for seller and listing performance: define KPIs, build/maintain data models and ELT pipelines, run experiments and impact measurement, deliver dashboards and automated reporting, use AI-assisted tools and analytics agents, and mentor junior analysts to drive business outcomes.

As a Senior Data Scientist, you will design, build, and operate machine learning and GenAI systems, influence technical direction, and mentor junior data scientists, focusing on production ML systems.

The Marketing Data Analyst will design data models, implement analytics solutions, and deliver insights for marketing optimization. They will work across channels and support data-driven decision-making by partnering with teams and stakeholders, leveraging AI tools for efficient analytics.

Lead high-impact AI initiatives, develop ML products, collaborate with cross-functional teams, and mentor data scientists in a dynamic environment.

The Manager of Data Management leads a team, oversees data processes, ensures data integrity, collaborates with IT, and supports compliance for the organization's data management systems.

As a Data Engineer, you will design and implement solutions for data integration and manage data quality, while collaborating with various teams for optimal data usage and decision-making.

Lead R&D data science projects from experimentation to production, using PySpark/Scala and ML to analyze listener behavior, develop explainable models, collaborate cross-functionally, document results, and present findings to stakeholders.

The Data Engineer will design and build robust data pipelines using Microsoft Fabric and Azure services, focusing on both structured and unstructured data processing, ensuring data quality and supporting analytics and AI-driven insights.

The Associate Director of Data Platforms will operationalize the enterprise data platform strategy, ensuring alignment between business needs and technical architecture across multiple data platforms like Microsoft Fabric and Snowflake.

Drive causal and machine learning analyses to measure product feature impacts, develop KPIs, and create visualizations while collaborating with cross-functional teams.

Executive leader responsible for enterprise technology, data and AI strategy, operating model and ERP transformation. Build scalable data platforms, advance analytics, automation and LLM capabilities, ensure governance and security, drive business partnering and measurable value, and lead a global technology and data organization.

Lead HR data analysis, integrity, migration, and reporting for Workday and related systems. Define requirements, build custom reports, establish reporting governance, execute testing and release management, and train team members. Collaborate cross-functionally on integrations, system enhancements, and data assurance to enable HR decision-making and workforce planning.

The Senior Data Analyst will analyze complex datasets, develop actionable insights, optimize business performance, and present findings to stakeholders in Canada.

Design and build scalable production data pipelines and warehouse models to support AI, analytics, and product teams. Own ingestion, transformation, testing, monitoring, and data quality. Build shared platform capabilities, collaborate cross-functionally, and define practices and cloud architecture for a new regional data engineering team.