Top Data Jobs in Toronto

Leads electrical design and delivery for hyperscale, colocation, and mission-critical data centers across North America and globally. Responsibilities include medium- and low-voltage distribution, standby generation, UPS, load analysis, power modeling, engineering documentation, technical reviews, code compliance, client coordination, mentoring, budgeting, scheduling, and business development. The role also serves as an Engineer of Record when applicable and supports multidisciplinary project teams, site visits, commissioning, and practice growth.

Leads multidisciplinary design delivery for hyperscale, colocation, and mission-critical data centers from site due diligence through construction administration. Coordinates architecture and engineering disciplines, manages design quality, schedules, requirements, risks, changes, documentation, and stakeholder communication. Oversees design reviews, proposals, client relationships, and technical delivery while mentoring staff and expanding AECOM’s Canadian data center practice.

Design and deliver mechanical systems for hyperscale, colocation, and other mission-critical data centers. Responsibilities include HVAC and cooling-system design, load calculations, equipment sizing, technical documentation, code compliance, multidisciplinary coordination, design reviews, and construction-phase support. The role also involves coordinating with clients, contractors, vendors, and utilities while addressing reliability, redundancy, resiliency, maintainability, energy efficiency, and capacity requirements.

Supports data transformation initiatives by building and maintaining ETL/ELT pipelines, automating data ingestion, validating data quality, and transforming datasets using SQL or Python. Collaborates with business stakeholders and data professionals across multiple departments, while documenting workflows, data dictionaries, and processes. This role provides hands-on data engineering experience during an eight-month internship.

Design complex data and workflow experiences for Braze’s enterprise customer engagement platform. Conduct user research, create high-fidelity prototypes, refine the design system, and collaborate with product, engineering, research, and analytics teams. Influence product roadmaps and deliver intuitive tools for data modeling, ingestion, integrations, and campaign execution. The role requires systems thinking, strong interaction and visual design, technical collaboration, and evidence-based decision-making.

Designs, develops, and optimizes cloud data platforms, pipelines, ETL/ELT processes, integrations, and migration solutions. Implements DataOps, CI/CD automation, metadata, lineage, governance, data quality, and monitoring capabilities. Supports cloud modernization, large-scale data migrations, data warehouses, lakes, and lakehouses while collaborating with architects, modelers, and governance teams. The role is fully onsite in Toronto for a 12-month engagement.

Analyze vulnerability and remediation data to produce executive KRIs, operational KPIs, dashboards, scorecards, and actionable risk insights. Validate data quality, maintain metric definitions and lineage, investigate reporting discrepancies, identify remediation trends and control gaps, and support audit, regulatory, and governance requests. The role uses ServiceNow Security Operations, analytical tools, APIs, SQL, Python, and PowerShell to automate reporting and improve vulnerability governance.

Defines and evolves data and AI product portfolios, setting product vision, roadmaps, requirements, priorities, success metrics, and responsible AI practices. Leads discovery, experimentation, validation, deployment monitoring, and continuous improvement while balancing business value, technical feasibility, security, compliance, data quality, and user experience. Collaborates with multidisciplinary technical and business teams, communicates recommendations and risks to stakeholders, and identifies opportunities involving generative AI, intelligent agents, RAG, automation, advanced analytics, and data platforms.

Designs and delivers production-grade data engineering, analytics, and automation solutions for investment and asset-management domains. Responsibilities include building pipelines, data models, APIs, workflows, and governed analytical products; integrating diverse data sources; implementing quality controls, reconciliation, lineage, monitoring, and observability; and translating complex business requirements into scalable technical solutions. The role also provides technical mentorship and requires strong knowledge of investment data, cloud platforms, modern engineering practices, and AI-enabled automation.

Designs, develops, modernizes, and operates scalable data platforms, pipelines, and data products across cloud and hybrid environments. Builds batch and near-real-time solutions using Python, SQL, AWS, PySpark, and related technologies; defines architecture, data models, governance, observability, and quality controls. Provides technical leadership through design reviews, mentoring, production support, and modernization of legacy ecosystems. Partners with investment, business, and technology stakeholders to deliver trusted, audit-ready asset management data products.

Maintains vendor master data across ERP systems, supporting supplier onboarding, data creation, maintenance, deactivation, issue resolution, synchronization, audits, and compliance. Produces KPI reports, dashboards, and analyses; identifies root causes and process improvements; maintains governance standards and documentation; supports data-loading tools; facilitates meetings; delivers training; and collaborates with suppliers, hospitals, and internal teams.

Collects and edits onsite building data using the Planitar iGuide Camera System to produce Virtual Location Surveys, digital floor plans, photo galleries, and technical reports. Conducts building science investigations across Canada, documents findings, and supports engineering, workspace, construction, and real estate clients. The role requires site work, strong communication and computer skills, independent work, a valid driver’s license and vehicle, working-at-heights certification, applicable security screening, and 40–60% travel.

Develop and operationalize generative AI, Copilot, and agentic solutions for Finance. Partner with stakeholders to identify use cases, prepare data and semantic models, engineer prompts and retrieval approaches, evaluate solution quality, analyze datasets, communicate insights, and monitor deployed solutions. Support analytics modernization, Alteryx workflows, responsible AI practices, and cross-functional implementation from problem definition through operationalization.

Supports data governance through master and transactional data maintenance, cleansing, validation, standards management, and issue resolution across ERP systems. The role gathers business requirements, coordinates data steward committees, monitors compliance, facilitates training, produces KPIs, reports, and dashboards, and recommends process improvements. It also serves as a subject matter expert and liaison for technical and non-technical stakeholders.

Senior Data Scientist focused on financial services and auto financing. The role analyzes customer financing journeys, builds affordability and intent models, owns pre-qualification analytics, designs experiments, and measures financing impacts on conversion, order value, and customer lifetime value. The scientist partners with product, marketing, engineering, data engineering, and external financial services teams to shape financing tools, customer experiences, and business strategy.

Partner with sales teams to win strategic data security opportunities. Deliver technical presentations, gather customer requirements, lead product evaluations, configure solutions, and design test plans. The role requires extensive pre-sales experience, security and networking expertise, data protection knowledge, AI fluency, and collaboration across matrixed go-to-market and partner teams.

The Associate Director will lead complex data centre projects, manage client relationships, mentor teams, and oversee project governance.

Designs and develops database systems, data models, schemas, data pipelines, and ETL processes. Responsibilities include optimizing SQL and PL/SQL, maintaining Oracle databases, improving performance and scalability, managing security and backups, troubleshooting database issues, supporting deployments, and documenting technical work. The role also collaborates across Agile teams to define requirements and technical specifications.

Designs and develops Oracle database systems, data models, schemas, ETL processes, and data pipelines. Responsibilities include optimizing SQL and PL/SQL, maintaining Oracle databases, supporting cloud technologies, monitoring performance, managing security and recovery, automating deployments, and documenting technical work. The role collaborates across Agile teams to define database requirements and improve scalability, integrity, and downstream reporting integrations.

Build production-ready AI applications, agents, APIs, data pipelines, integrations, and analytical models using Azure and Databricks. Develop LLM solutions with RAG, embeddings, vector search, tool calling, and agentic workflows. Establish testing, monitoring, governance, security, and responsible AI practices while partnering with consultants and subject-matter experts to translate business needs into reliable technical solutions.

Build production AI applications, agents, APIs, data pipelines, data models, and analytical solutions using LLMs, Azure, and Databricks. Develop RAG and agentic workflows, integrate structured and unstructured data, and implement testing, monitoring, security, governance, and responsible AI practices. Partner with consultants and subject-matter experts to translate business needs into practical solutions and move prototypes into reliable, scalable tools.

Build and productionize AI-powered applications, agents, APIs, data pipelines, integrations, and analytical models. The role combines LLM application development, RAG, data engineering, Azure and Databricks architecture, testing, monitoring, security, governance, and software delivery. Responsibilities also include partnering with consultants and subject-matter experts to translate business needs into practical AI solutions, prototype with users, and deploy reliable, scalable tools.

Develops executive presentations, board materials, strategic updates, dashboards, and visual narratives that translate complex business information into actionable insights. Partners with executives and stakeholders to communicate strategic priorities, performance metrics, transformation initiatives, risks, and outcomes. Establishes presentation standards and provides expertise in storytelling, data visualization, and executive communications while managing multiple high-quality deliverables.

Own the end-to-end data architecture for contact center telephony and workforce-management data. Design Bronze/Silver/Gold lakehouse flows, Kimball dimensional models, ingestion and watermarking strategies, reconciliation, integrity controls, orchestration, and failure recovery. Build and optimize Databricks and Delta Lake pipelines integrating Snowflake, APIs, SFTP, and vendor files. Support parallel-run migration, legacy decommissioning, and communicate architecture decisions to engineering and business stakeholders.

Designs, builds, automates, and optimizes enterprise cloud data pipelines and platforms. Responsibilities include ETL/ELT, DataOps, CI/CD, data migration, data modeling, data warehousing, lakehouse architecture, database administration, performance tuning, and production support. The role uses Databricks, Azure services, PySpark, SQL, and multiple cloud platforms to deliver scalable structured and unstructured data solutions.