Top Data Jobs in Toronto

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

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.

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.

Performs Structured Finance security mappings, data quality controls, reconciliations, regulatory dataset validation, and data issue resolution. Maintains metadata, data dictionaries, and data lineage while supporting data governance and AI-ready initiatives. Collaborates with Data, Credit Operations, Technology, and global stakeholders to ensure accurate, complete, timely, and compliant ratings and regulatory data.

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.

Build Python-based frameworks to validate, reconcile, cleanse, and transform large datasets. Use Pandas and NumPy to identify data quality issues, discrepancies, duplicates, and anomalies. Automate validation workflows, investigate root causes, produce data quality reports, optimize processing, and collaborate with business, analytics, and engineering teams on data migration, onboarding, and integration initiatives.

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.

Leads Zynga’s global centralized data platform and product strategy, overseeing real-time ingestion, cloud data warehousing, experimentation, metrics, governance, and AI/ML capabilities. Manages approximately 60 employees across multiple regions, including senior engineering, product, and program leaders. Drives AI adoption, organizational change, platform reliability, data quality, compliance, and self-service analytics while partnering with studio and executive stakeholders.

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.

Own the market data product strategy supporting front-office trading and EMS capabilities. Consolidate data providers, reference data, gateways, and related services while defining ontology, standards, and common data models. Manage requirements, prioritize initiatives with business, architecture, and technology stakeholders, and help deliver the target-state market data platform and roadmap. The role requires strong financial-services market data expertise and experience with OMS, EMS, and trading technology.

Build and operate Databricks-based data products and pipelines that create, model, and deliver syndicated and custom consumer audience segments. Develop tables, views, jobs, workflows, and real-time processing solutions using Python, SQL, Spark, and Delta Lake. Support marketplace delivery, reporting, observability, metadata, testing, CI/CD, privacy compliance, and AI-assisted development. Partner with data science and commercial teams to translate business requirements into scalable engineering solutions, while promoting architecture and engineering best practices.

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

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Designs end-to-end enterprise data architectures spanning data sources, integration, transformation, storage, governance, and consumption. Maps complex data landscapes, defines scalable integration patterns across applications, APIs, databases, cloud services, and third-party sources, and guides delivery teams to ensure alignment with data standards, security, quality, and technology strategy. The role also supports modernization, migration, and transformation initiatives, particularly for financial services and other regulated industries.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Analyze business performance, consumer behavior, market trends, and KPIs to generate actionable insights. Partner with business leaders to shape consumer strategy, support dashboards and reporting systems, and present recommendations to cross-functional stakeholders. The role requires strong SQL, scripting, business analytics, BI, communication, and data interpretation skills, with preferred experience advising strategy teams and using AI tools to improve analytical workflows.

Lead design and delivery of large-scale AI/ML and retrieval systems for cybersecurity. Build end-to-end ML architecture, models (anomaly detection, ranking, RAG/LLM), and production infrastructure. Drive technical strategy, cross-functional initiatives, experimentation, and mentor junior scientists while balancing latency, scalability, reliability, and business impact.

The Data Engineer will develop and maintain data pipelines, ensure data quality, and collaborate with business teams to implement insightful data solutions.

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.