Top Data Jobs in Toronto

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Lead Data Scientist analyzing global fraud data to identify patterns, develop predictive models, and prevent payment fraud. The role builds fraud-related products and services, extracts insights from large datasets, collaborates with stakeholders, and presents findings to management and clients. Required skills include Python, Spark, complex data queries, and advanced data analysis. Experience with agentic AI systems and Unix is preferred, along with payments or fraud expertise.

Supports sales, customer success, and product development for Mastercard Identity through data analysis, machine learning, rules-based fraud models, and proof-of-value testing. Advises customers on fraud workflows, analyzes data features, supports API integration QA, and provides product recommendations. Builds customer relationships, translates business needs into technical solutions, contributes to product specifications and training, and serves as a subject matter expert on identity data and products.

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Designs and maintains reliable data pipelines and computed tables using SparkSQL and PySpark. Integrates IoT, product, customer, external, unstructured, and ML-related data into scalable data models supporting analytics, causal inference, model training, and dashboards. The role requires ETL development, data orchestration, API integration, strong data quality and uptime, and collaboration with Data Science, AI/ML, Analytics, and engineering teams. This is a remote position for candidates residing in Canada.

Designs, develops, validates, and monitors advanced analytics, statistical models, machine learning solutions, and data products. Develops algorithms for sample design, weighting, calibration, data integration, and production improvements. Reviews internal and external models, supports cross-functional decision-making, and communicates analytical findings to technical and non-technical stakeholders. The role also contributes to model deployment, automated data pipelines, and large-scale behavioral data analysis.

Designs and operationalizes enterprise AI, machine learning, and data solutions using Snowflake, Databricks, and Azure. Responsibilities include developing ML models, scalable data pipelines, MLOps and DevOps workflows, AI-ready data products, agent integrations, model deployment, monitoring, and data quality processes. The role also involves collaborating with business and technical stakeholders to deliver production solutions for client problems. This is a hybrid position requiring three days per week in a Mississauga or Montreal office or client location.

Supports client analytics engagements through dataset analysis, dashboard and report development, KPI tracking, data validation, documentation, visualization, and communicating insights. Collaborates with senior analysts and data engineers while developing consulting, data modeling, reporting, and analytical workflow skills.

Designs and engineers enterprise data models, pipelines, and data products supporting analytics and data science across business units. Ensures data quality, governance, integrity, metadata standardization, and performance optimization. Partners with business stakeholders, data analysts, data scientists, governance teams, and risk partners to translate complex requirements into production-ready solutions and derive business insights from large datasets.

Develop and implement data science and machine learning solutions for Canadian business units. Translate business goals into analytical problems, build and evaluate models, perform feature engineering, and use AWS services including SageMaker and Glue. Collaborate with stakeholders, communicate insights, create technical designs, and support analytics innovation. Preferred work includes GenAI, LLM APIs, RAG, vector databases, Docker, and CI/CD.

Leads data platform governance, application delivery, production support, technology initiatives, and solution design. Directs technical teams, manages projects, budgets, vendors, infrastructure operations, and stakeholder relationships. Coaches employees, advises application owners, coordinates cross-functional workstreams, drives innovation, and ensures reliable, high-quality technology implementations aligned with business requirements.

Supports enterprise data governance and management initiatives across business units. Responsibilities include managing governance artifacts, facilitating cross-functional forums, capturing metadata and data quality requirements, coordinating issue and change management, prioritizing remediation, documenting requirements, and advising data owners and stewards. The role also tracks resolutions, assesses business risks, develops recommendations, and communicates progress to stakeholders while promoting compliance with governance standards and policies.

Build and scale backend-focused software for Braze’s petabyte-scale Data Lakehouse. Develop data pipelines, asynchronous processing, ETL applications, and end-to-end features across platforms such as Snowflake, ClickHouse, and Databricks. Drive architectural decisions, contribute to technical strategy, and deliver scalable web applications handling high volumes of data and traffic in an agile environment.

Build and maintain backend-focused, end-to-end software for Braze’s petabyte-scale Data Lakehouse platform. Develop infrastructure and tools supporting Snowflake, ClickHouse, and Databricks, manage high-volume event data, and deliver front-end experiences when needed. Collaborate across teams, apply strong software design and algorithms knowledge, and help scale systems handling billions of daily rows and significant customer and internal workloads.

Leads clinical data management activities from study start-up through database lock, including documentation, system setup, data validation, reconciliation, timelines, budgets, quality, and sponsor requirements. Provides study team leadership, mentoring, coaching, risk mitigation, client relationship management, audit support, SOP input, and process improvement. Coordinates with project management, EDC design, SAS programming, statistics, and pharmacokinetics teams while ensuring compliance with protocols, GCP, and applicable standards.

Analyze complex enterprise data and translate business needs into scalable reporting, dashboards, and business intelligence solutions. Lead discovery sessions, define analytical requirements and success metrics, write advanced SQL, develop self-service analytics using BI platforms, and communicate actionable insights to stakeholders. The role also applies data storytelling, process optimization, AI-enabled analytics, and emerging technologies to improve decision-making and operational performance.

Leads enterprise data governance and data risk control initiatives, including policy implementation, framework effectiveness, issue management, metrics reporting, taxonomy updates, regulatory responses, and audit action tracking. Partners with business lines and global functions to develop governance principles, prioritize enterprise processes, monitor adoption, and recommend improvements. Requires strong communication, stakeholder influence, project management, and the ability to manage ambiguity and competing priorities.

Build and maintain multi-cloud platform automation across Google Cloud and Azure, with a focus on BigQuery and enterprise data services. Develop orchestration code, infrastructure-as-code templates, APIs, SDKs, webhooks, and automated tests using Python, Terraform, and CI/CD tools. Manage IAM, security controls, PKI, networking, and compliance requirements. Collaborate with cloud, security, network, risk, and business teams in an Agile Scrum environment to improve self-service platform capabilities and integrations.

Develops fraud detection, prevention, and investigation models using statistical analysis, machine learning, deep learning, and large-scale data processing. Responsibilities include collecting and integrating structured and unstructured data, identifying financial crime patterns, developing predictive solutions, monitoring model performance, resolving analytical issues, and communicating insights to stakeholders. The role also supports data and AI tool development, training, and innovation across the Financial Crimes Unit.

Lead technical delivery for client projects by designing, implementing, and scaling high-performance kdb+ architectures for billions of time-series data rows. Develop, test, support, and maintain client applications while collaborating with quants, traders, and analysts. Mentor developers, make technical decisions, and drive innovation across kdb+, q, and related technologies in capital markets environments.

The Data Analyst will translate business needs into scalable analytics solutions, develop dashboards and business intelligence using tools such as Looker or Tableau, write advanced SQL, analyze enterprise data, and communicate actionable insights. Responsibilities include leading discovery workshops, defining metrics, improving business performance through trend and root-cause analysis, supporting self-service analytics, using AI-enabled tools, and identifying opportunities for process and analytics innovation.

Manage data security operations, including sensitive data discovery, classification, protection, remediation, encryption, and data security posture management. Advise business and technology stakeholders on data exposures, controls, privacy, governance, and risk-based remediation. Support Microsoft Information Protection activities, reporting, executive communications, and security initiatives across the organization.

Leads end-to-end data science and machine learning initiatives supporting growth, pricing, customer engagement, recommendations, experimentation, and decision-making. Responsibilities include feature engineering, model development, validation, deployment, monitoring, productionization, cloud migration, pipeline modernization, and experimentation analysis. The role partners with business and technical teams, communicates insights, develops reusable analytical assets, and mentors colleagues.