Top Data Jobs in Toronto

Build production AI harness systems for Kinaxis’s Databricks-based enterprise Data Fabric. Responsibilities include developing agent execution frameworks, integrating LLMs with governed data, implementing tool calling and workflow controls, enforcing multi-tenant isolation, and delivering APIs, SDKs, libraries, templates, and operational runbooks. The role requires distributed-systems engineering, cloud data-platform experience, secure multi-tenant design, and practical expertise in agent orchestration, retrieval, evaluation, guardrails, metadata, lineage, and governance.

Conduct outbound prospecting to identify and engage potential data partners. Research target accounts and decision-makers, personalize outreach, qualify partnership opportunities, and create substantive handoffs for the Data Partnerships Lead or Account Executive. Maintain accurate CRM records, follow up consistently, surface data rights and delivery considerations, and improve targeting and messaging based on feedback, response patterns, and objections.

Own growth marketing data science for a key domain, developing optimization algorithms, attribution and incrementality models, experimentation strategies, analytics pipelines, dashboards, and strategic insights. Partner with marketing, product, engineering, data engineers, and ad platforms to improve customer acquisition, campaign performance, and marketing ROI. Serve as the end-to-end data domain owner and translate complex analyses into business decisions.

Oversee performance data across employment services programs, developing and maintaining Power BI reports, data models, dashboards, and enterprise reporting. Analyze operational performance through forecasting, modeling, and evaluation; establish KPIs and targets; identify improvement opportunities; and deliver actionable insights to leaders and partners. The role promotes data-driven decision-making, ensures data quality and governance, and translates complex information into recommendations for organizational performance and continuous improvement.

Build and maintain reports, dashboards, scorecards, and analytical outputs. Analyze large datasets and business trends across product, onboarding, operations, revenue, sales, and customer experience. Use SQL, Excel, and BI tools to validate data, identify issues, improve reporting, and provide actionable recommendations. Partner with stakeholders to define metrics, support reporting cycles, document data processes, and contribute to automation and modern data platform initiatives.

Establish and lead a centralized procurement function covering sourcing, vendor selection, negotiation, contracting, forecasting, governance, compliance, and supplier performance. The role will optimize total cost of ownership, manage supply chain risks, standardize facility services, and ensure timely delivery of construction materials and equipment. It requires extensive procurement leadership, infrastructure construction experience, strong vendor negotiation skills, and the ability to communicate complex information to executive stakeholders.

Provide hands-on technical leadership for Benevity’s data platform. Design and build scalable streaming and batch pipelines, AWS lakehouse and medallion architecture, metadata governance, semantic layers, and data products. Establish configuration-driven workflows, data contracts, quality standards, observability, and automated CI governance. Partner with architects and product stakeholders, develop AI-assisted engineering tools, document architecture, deliver proofs of concept, and mentor engineers while guiding platform modernization.

Lead the architecture and hands-on engineering of Cloudbeds’ Snowflake and dbt data platform. Own data modeling, warehouse design, ingestion, orchestration, DataOps, CI/CD, governance, observability, performance, cost optimization, and AI/ML readiness. Mentor analytical engineers, evaluate emerging tools, establish data quality and security standards, and collaborate with engineering, infrastructure, data science, and business stakeholders in a fully remote environment.

Own and evolve HOOPP’s investment data warehouse, pipelines, enterprise data model, and production support processes. Investigate data and platform issues, perform root-cause analysis, maintain data integrity and governance, and deliver sustainable solutions across investment, risk, finance, and technology teams. Develop AI-assisted projects using Python and related tools, improve monitoring and operational workflows, and manage access reviews, releases, documentation, and preventive actions.

Build and operate scalable AWS data foundations supporting analytics, search, and AI use cases. Develop structured and unstructured data pipelines using Snowflake, S3, lakehouse architectures, and distributed processing. Establish engineering standards, automated testing, infrastructure-as-code, CI/CD, data quality, lineage, and observability. Collaborate with data scientists, AI engineers, and business partners to productionize ideas, mentor engineers, and deliver secure, reliable, measurable solutions.

Develop and scale customer-facing analytics and reporting. Build dashboards, reports, and data tools; translate customer and business needs into data requirements; support product analytics and internal intelligence; resolve reporting issues; present insights to customers; and drive cross-functional data initiatives from problem definition through delivery.

Analyze and integrate transportation and ecommerce data across SQL Server, Oracle, IHUB, Snowflake, and enterprise warehouses. Build dashboards and reporting solutions, support data migration, improve data quality and governance, model processes, and deliver insights for courier capacity, service levels, cost reduction, and decision-making. Lead analytical initiatives using Agile and Waterfall methodologies while collaborating with ecommerce, supply chain, and external stakeholders.

Leads IPEX’s North American data center strategy and multi-year growth roadmap. Identifies priority applications, customers, technologies, geographies, and routes to market across infrastructure, cooling, and water management. Builds relationships with hyperscalers, colocation providers, engineers, developers, contractors, and technology partners; influences specifications and design decisions; identifies portfolio gaps; and aligns Sales, Product Management, Innovation, Technical, Marketing, and Operations around execution, KPIs, pipeline, and investment priorities.

Leads data migration and integration across SQL Server, Oracle, IHUB, enterprise warehouses, and Snowflake. Develops data models, pipelines, dashboards, and reporting using Power BI, ThoughtSpot, and Tableau. Supports ecommerce transportation and supply chain process improvement through analytics, simulation, stakeholder collaboration, project management, change initiatives, documentation, and business reviews. Acts as a liaison and subject matter expert to improve courier capacity, service levels, cost effectiveness, and decision-making.

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.

Leads requirements gathering, data analysis, and financial data integration for transactional data warehouses. Translates finance and technical requirements into documented data transformation solutions, collaborates with multidisciplinary teams, supports testing, tracks delivery, and presents solutions to stakeholders. Requires extensive business analysis experience in finance or financial systems, strong ETL, SQL, data modeling, database, and ERP knowledge, plus excellent communication and project management skills.

Own end-to-end delivery programs for robotics and embodied AI datasets. Coordinate Sales, Data Operations, Labeling, and Engineering to meet customer specifications, volumes, quality standards, and timelines. Build scripts, QA checks, packaging tools, manifests, dashboards, and reporting to automate delivery workflows. Define dataset standards, monitor metrics, manage escalations, and serve as a senior customer-facing delivery lead for AI-lab accounts.

Designs, develops, tests, and maintains data integrations connecting customer cloud and on-premises systems to the Prophix platform. Builds reusable APIs and real-time interfaces, contributes to integration architecture and strategy, automates reliable data processes, and supports stakeholders and engineers. The role also involves project management, technical leadership, mentoring, enforcing integration standards, and helping customers transition to secure, scalable cloud-based data environments.

Own the strategy, roadmap, commercial performance, and delivery of Pelmorex’s data product portfolio. Lead product managers, guide the underlying data platform, launch privacy-safe data collaboration and clean room capabilities, and translate privacy regulations into product requirements. Partner with engineering, legal, sales, clients, and data providers to drive revenue, measurement, attribution, partnerships, pricing, and market positioning across advertising data products.

Leads the Investment Finance & Operations Data Governance Program, including roadmap development, governance framework oversight, data risk assessments, policy and standards creation, data asset cataloging, metadata and lineage management, and data quality monitoring. Partners with senior leaders, investment and operations teams, and enterprise data stakeholders to assign governance responsibilities, support transformation initiatives, mature data capabilities, and mitigate data risks.

Build and own production data pipelines, dbt models, semantic layers, and supporting infrastructure across Snowflake, BigQuery, Airflow, Fivetran, Databricks, AWS, and GCP. Partner with stakeholders across business functions to scope requirements, resolve data quality issues, monitor SLAs, support incident response, and apply data governance practices. The role requires autonomous delivery, strong communication, production-grade Python and SQL, and contributions to engineering standards, CI/CD, testing, and documentation.

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

Build and maintain scalable data pipeline architecture, process complex datasets, improve data delivery and infrastructure, and support data initiatives across engineering, product, business intelligence, and data science teams. Develop extraction, transformation, and loading infrastructure using Spark, EMR, Kafka, and other big data technologies while optimizing production-scale data and machine learning pipelines.

Designs end-to-end data solutions spanning ingestion, transformation, storage, modeling, and delivery for BI, analytics, AI/ML, and external consumers. Creates data flow diagrams and models, guides architectural and technology decisions, and collaborates with engineering, analytics, AI, governance, and business teams. Ensures solutions are scalable, secure, well-documented, performant, and compliant with financial-services governance and regulatory requirements using AWS, Snowflake, dbt, Alation, and Tableau Data Catalogs.

Leads integration of two major financial data product launches into the AlphaSense platform. Defines zero-to-one product lifecycles, user stories, milestones, roadmaps, and PRDs; translates investment research workflows into data architectures; manages resources and delivery across teams; mentors product managers; validates solutions with customers and engineering; and drives competitive intelligence, prioritization, and cross-functional alignment.