Top Data Jobs in Toronto

Own the product vision, roadmap, KPIs, and business outcomes for software products. Conduct customer discovery, translate insights into priorities, collaborate with technology, data, strategy, and design teams, and guide technical tradeoffs. Build stakeholder alignment, deliver customer-focused experiences, and use quantitative and qualitative analysis to refine product direction. The role requires at least three years of software product management experience and successful software product delivery.

Provides operational support for Global R&D digital systems and data platforms. Responsibilities include monitoring and resolving data quality issues, administering user access, validating integrations, developing reports and dashboards, documenting procedures, supporting users, and identifying process improvements. The role partners across R&D, Quality, Regulatory, Packaging, Procurement, and IT while using tools such as Power BI, Databricks, Excel, and Microsoft Copilot.

Lead BuildOps’ unified Data and AI engineering function, owning data platform architecture, pipelines, onboarding, governance, reliability, and ML infrastructure. Build and scale a high-performing data engineering organization, establish production-grade monitoring and incident practices, and deliver trusted data products for B2B SaaS customers. Define long-term data strategy, guide architectural decisions, and partner across technical and business teams while remaining hands-on with implementation and operational responsibility.

Supports the strategic management and use of Mastercard customer data and franchise assets. Develops reporting, insights, presentations, and recommendations; monitors data quality and asset utilization; supports go-to-market initiatives, technology adoption, requirements documentation, testing, and process improvements. Collaborates with regional, technology, franchise, and cross-functional teams to improve operational effectiveness, customer experience, and business outcomes.

Design and implement data engineering, governance, Collibra, metadata management, and data lineage solutions for financial services clients. Establish data quality standards, governance controls, ownership workflows, and regulatory data practices. Integrate and optimize data pipelines and governance tools while collaborating with architects, business stakeholders, and technology teams. The role requires strong SQL, ETL/ELT, data governance, and Collibra expertise, with optional cloud, DevOps, Agile, and financial services experience.

Design, develop, and optimize enterprise data pipelines and ETL/ELT integrations using Informatica IDMC. Build transformation workflows, integrate multiple data sources into cloud and enterprise environments, troubleshoot performance and data quality issues, and implement testing and monitoring. Collaborate with architects, analysts, engineers, and business stakeholders to deliver scalable data solutions, primarily for financial services clients.

Design and deliver enterprise data architecture solutions for financial services clients using Informatica IDMC and Collibra. Lead data integration, metadata management, data quality, governance, and lineage initiatives; establish architecture standards and controls; guide technical delivery teams; and translate client requirements into scalable solutions. Build stakeholder relationships, support consulting engagements, and ensure compliant, sustainable data environments.

Owns the end-to-end lifecycle of enterprise data products, including roadmap, requirements, governance, design oversight, data quality validation, testing, UAT, certification, releases, and retirement. Partners with business, analytics, governance, and technical teams to prioritize demand, manage data contracts, ensure product quality, and deliver trusted data products across a cloud data platform.

Leads the development and production deployment of advanced data science and machine learning solutions for cybersecurity, payments, and fraud. Designs scalable graph analytics, models complex risk patterns, builds data pipelines using Python, SQL, Spark, Hadoop, and Databricks, and establishes validation and monitoring strategies. Partners with engineering, cybersecurity, product, and operations teams, communicates insights to stakeholders, and manages and mentors junior data scientists.

Leads data engineering for cyber analytics by designing pipelines, ETL processes, storage, graph databases, and data models. Partners with data scientists and technology teams to integrate ML and AI solutions, manage production deployments, and build cohesive analytical datasets. Oversees and develops a team of data engineers, manages priorities, and collaborates with technical and business stakeholders.

Leads the audit strategy and oversight for the Applications Technology and Data portfolio. Provides independent challenge on technology, data, risk, governance, and control matters; advises senior executives; manages audit coverage and regulatory alignment; identifies emerging risks; and leads a high-performing audit team through coaching, succession planning, resource management, and continuous improvement.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

Own and deliver highly available, low-latency backend identity systems supporting consumer onboarding, authentication, and verification. Lead quarterly team goals, guide engineers through complex problems, collaborate with product and design stakeholders, improve technical and code quality standards, monitor system availability, support on-call operations, and mentor team members.

Serve as the technical anchor for Domino’s identity and authorization platform. Design scalable, relationship-based authorization using SpiceDB; guide migration from legacy access checks; build cloud-native identity integrations; improve platform reliability, security, and architecture; and advise engineering teams on authorization, APIs, abstractions, and AI-assisted development practices.

Lead data science initiatives for agentic AI products, partnering with data scientists, AI/ML engineers, and product managers. Build, deploy, validate, and optimize machine learning models using large-scale numeric and textual data. Design A/B tests, analyze telemetry, translate technical findings into business strategy, research emerging technologies, and develop talent while guiding enterprise-scale AI decisions.

Leads strategic pricing and monetization across sales-driven channels, managing pricing partners and advising on complex commercial deals. Develops pricing frameworks, approval processes, deal governance, and scalable tools using automation, AI, and data. Partners with Sales, Product, Engineering, Legal, Risk, and Revenue Operations to improve deal economics, profitability, pricing performance, packaging, and portfolio-level monetization strategy.

Lead presales technical discovery, design scalable solutions, develop proposals, statements of work, estimates, and delivery timelines, and support sales-to-delivery transitions. Collaborate with account executives, clients, partners, and technical teams across cloud, infrastructure, platform engineering, cybersecurity, data, and AI. Translate business needs into practical technology architectures, communicate with technical and business stakeholders, and improve solution offerings.

Leads the design, development, and optimization of scalable data architectures, ETL pipelines, ingestion frameworks, data quality processes, governance practices, and audit systems. Partners with cross-functional teams to translate requirements into data solutions, analyzes data for business insights, communicates recommendations, and mentors Data Engineers. The role also serves as a technical authority and supports continuous improvement across data engineering practices.

Build a high-performance frontend data platform for exploring and analyzing hardware telemetry. Develop React and TypeScript applications, interactive time-series visualizations, data visualization plugins, complex application state management, and LLM-assisted analysis workflows. Collaborate directly with hardware engineers and customers to define interfaces, prototype workflows, demonstrate features, and incorporate feedback into product development.

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

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, improve reliability and quality, and mentor engineers. Build and monitor production LLM and agent systems, automate workflows with AI, and deliver high-quality Python and SQL while validating AI-generated code. Partner with Marketing, Sales, BI, Data Science, and R&D teams.

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.

Manages adoption, localization, and scaling of enterprise data products for Takeda Canada. Leads solution-agnostic requirements discovery, business analysis, process and data mapping, and root-cause assessment. Translates analytics outputs into actionable business insights, develops Power BI dashboards, supports data quality and governance, and partners with global delivery, engineering, data science, and commercial stakeholders. Drives product adoption, backlog prioritization, stakeholder alignment, and continuous improvement across pharmaceutical commercial data initiatives.

Owns Takeda Canada's data roadmap, governance, stewardship, quality monitoring, documentation, and integration into enterprise data platforms and customer data systems. The role leads master data management, data quality remediation, standards alignment, source onboarding, consent controls, and data flow validation. It partners with business, technology, vendors, and delivery teams to enable analytics and business use cases, while improving data fluency through training, playbooks, and stakeholder support.

Leads enterprise data quality and governance initiatives, including data profiling, validation, cleansing, monitoring, metadata management, cataloging, lineage, classification, and data protection. Partners with business stakeholders, data owners, engineering, architecture, analytics, and security teams to define quality standards, remediate issues, improve governance-platform adoption, and establish data sensitivity controls. Provides technical leadership across global data environments and supports AI-enabled classification and responsible data use.