Top Data Jobs in Toronto

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

Data Analytics Engineering Intern supporting cross-functional teams by transforming data into data lake and Snowflake structures, maintaining data processes and pipelines, ensuring data quality and documentation, building automated reports and visualizations, and analyzing large datasets. The role involves solving business problems end to end, from identifying requirements through deploying practical data solutions.

Develop data structures and pipelines in data lakes and Snowflake, maintain data quality and processes, build automated reporting and visualizations, and analyze large datasets. Collaborate cross-functionally to identify business problems, recommend solutions, and support deployment while communicating technical concepts to nontechnical audiences.

Build and maintain data structures, pipelines, reporting, visualizations, and data processes using data lake and Snowflake technologies. Ensure data quality, documentation, and usability while performing investigative analysis on large datasets. Collaborate cross-functionally to identify business problems, recommend solutions, and support end-to-end deployment. The role requires a quantitative bachelor’s degree, coding experience, SQL or dbt familiarity, and data pipeline experience.

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.

Lead design and build scalable data architectures, ETL pipelines, ingestion and data quality frameworks. Collaborate with cross-functional teams, enforce data governance, analyze data for insights, mentor junior engineers, and serve as the technical authority on data engineering best practices.

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

Leads the Client Experience Office’s Chief of Staff function, driving strategic execution, executive decision-making, governance, performance management, analytics, reporting, communications, and AI initiative alignment. Partners across enterprise functions to manage priorities, risks, resources, and business outcomes. Supports executive and board forums, develops dashboards and scorecards, and leads a high-performing team focused on transformation, client experience, and organizational effectiveness.

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.

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.

Lead data science initiatives for Risk and Support Operations by analyzing customer and product data, defining metrics, forecasting demand, evaluating experiments and risk policies, and building decision frameworks. Use AI tools and agent workflows to improve analytical efficiency. Partner with product, engineering, and risk teams, communicate insights to senior stakeholders, establish technical standards, mentor data scientists, and support hiring.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, workflow automation, evaluation, and observability capabilities. Work hands-on with Python, SQL, dbt, lakehouse data, cloud infrastructure, model selection, and inference-cost optimization. Establish reusable engineering patterns and mentor teams through high-quality code and system design.

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.

Analyze product, customer, risk, and support data to guide decisions across Block’s platforms. Own metrics, forecasting, staffing models, experiments, visualizations, and measurement frameworks. Evaluate risk models and policies for performance, bias, and drift; use AI tools and agent workflows to improve analytical execution. Partner with product, engineering, and risk stakeholders, lead technical standards, build reusable tooling, mentor data scientists, and contribute to hiring.

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.

Drive strategic federal sales growth across DISA 4th Estate and Intelligence Community accounts. Responsibilities include pipeline development, account planning, opportunity and win strategy, stakeholder relationship development, acquisition navigation, RFI and solicitation support, CRM management, forecasting, and translating technology solutions into mission outcomes. The role requires expertise in long-cycle federal sales, procurement processes, secure environments, and technology solutions.

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.

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.