Top Data Jobs in Toronto

Perform Sage Intacct data migrations, ETL, validation, reconciliation, cleanup, integrations, customizations, workflows, and automations. Support ERP implementation projects, troubleshoot data and web-service issues, create reports and templates, and train customers on data management tools and best practices. Collaborate with clients and internal teams to solve data challenges, estimate project requirements, and integrate Sage Intacct with CRM, construction management, payroll, and other systems. The role is remote with travel up to 20%.

Analyze go-to-market performance across marketing, sales, revenue operations, and finance. Build reporting connecting campaigns to pipeline and revenue, investigate funnel and attribution questions, support budget analysis and planning, improve data quality, and communicate insights to technical and non-technical stakeholders. Collaborate with AI and Data Engineering teams on marketing data agents, architecture, and source tables.

The Data Scientist 2 will engage in the full data science lifecycle, including data extraction, model development, and deployment to meet business needs.

As a Data Scientist II, you will build and deliver data science solutions, validate models, support teams, and communicate insights to stakeholders.

Own and drive the architecture and roadmap of ClickUp's data platform. Build scalable, reliable data pipelines and AI/ML infrastructure using AWS serverless, Snowflake, dbt, and Terraform. Lead cross-team technical initiatives, optimize cloud costs, establish engineering standards (observability, testing, CI/CD), mentor senior engineers, and influence org-wide architecture decisions.

Owns the technical strategy, roadmap, requirements, and delivery of scalable data solutions. Partners with business, product, design, and engineering teams to prioritize high-impact features, assess vendors, guide architecture, incorporate AI strategies, and resolve technical issues. The role requires strong BI and data warehouse expertise, technical leadership, stakeholder communication, agile product development, customer delivery support, and mentoring responsibilities.

Lead design, build, and operate scalable Generative AI systems (LLM apps, RAG pipelines, multi-agent systems) in production using Databricks and cloud infrastructure. Own CI/CD, monitoring, evaluation frameworks, optimize retrieval/generation, mentor engineers, and influence product roadmap.

Own go-to-market strategy and adoption for Stripe's data products. Develop positioning, messaging, competitive intelligence, launch plans, demand programs, and sales enablement. Translate customer and market insights into roadmap recommendations and measurable KPIs while partnering cross-functionally to drive commercialization and growth.

Design, model and implement enterprise data integration and ETL solutions using Oracle Hyperion Planning, Essbase, Oracle BI and Oracle Data Integrator. Analyze and cleanse source data, build automated extraction/transform/load processes, scheduling, monitoring and error notification. Provide full life-cycle data conversion, reporting, and automated distribution for centralized financial planning and budgeting systems. Work with business and IT stakeholders to design resilient, multi-tier solutions aligned with Government of Ontario IT standards.

Design, implement and maintain data integration and data quality solutions using Informatica PowerCenter/PowerExchange/IDQ, Oracle databases, and Unix shell scripting on AIX/Linux.

Lead development and maintenance of the core data platform: build performant pipelines, data warehousing, and ML infrastructure; partner with data scientists to produce production datasets; ensure data governance, quality, lineage, and accessibility; integrate cross-platform data sources; automate reporting and deliver stakeholder insights to enable data-driven decisions.

Perform hands-on analytics for live-service games: analyze player behavior, retention, monetization, and economy health; build and maintain dashboards; write complex SQL workflows; partner across product, live ops, finance, and engineering; run experiments and modeling; define KPIs and improve data quality; present insights and recommendations to stakeholders.

Own and maintain data platform infrastructure: provision Snowflake via Terraform, build CI/CD for dbt, implement data quality and observability, extend the OpenMetadata catalog, create developer tooling and reproducible environments, and ensure secure, production-ready data pipelines.

Lead and grow a Data Engineering team to modernize enterprise data platforms (Snowflake/Databricks), deliver secure scalable batch and real-time pipelines, enable DataOps/MLOps, establish CI/CD and IaC practices, monitor platform health, and drive strategic platform migrations and performance optimizations with cross-functional stakeholders.

Lead the development and delivery of AI-driven product capabilities, transforming data into actionable insights while mentoring a high-performing team of data scientists and engineers.

As a Data Engineer, you will design and implement scalable data pipelines and solutions, collaborate with teams, and apply new technologies in an Agile environment.

The Data Engineer will design scalable data pipelines, manage data solutions, collaborate with teams, and ensure project delivery on time while applying new technologies.

Design, develop, and optimize big data solutions using Databricks, Apache Spark, and cloud platforms. Collaborate with teams to ensure high-performance data architectures.

The VP, Head of Policy & Data Operations manages data governance and operations for proxy voting policies, ensuring data integrity and operational excellence while collaborating across teams to optimize processes and manage risks.

Design, build, and optimize lakehouse data pipelines and curated data products using Python and SQL. Improve performance, observability, governance, and cost-efficiency. Own data modeling, orchestration, data quality, and secure access patterns. Partner with product, engineering, and analytics teams, troubleshoot production issues, and mentor junior engineers to advance the organization’s data platform and AI readiness.

The Data Scientist supports customer use of the Epistemix platform through data integration, modeling, and visualization, improving decision-making and customer satisfaction.

Lead and grow SQUIRE's Data organization as a player-coach, owning long-term data architecture, building reliable Snowflake pipelines, setting standards for modeling and data quality, hiring and budgeting for a 5+ person team, partnering cross-functionally, auditing integrations, and enabling AI/LLM use cases while remaining hands-on for high-risk technical work.

Evaluate and label model-generated and human-written code outputs, debug and assess code/agent trajectories, prompt models for complex data science tasks, review visualizations, and report quality trends to improve model behavior.

Build and operate governed, reproducible AWS-based data pipelines and analytical products. Stand up managed AWS foundation, deliver top-priority data products, support product teams to follow architecture, and apply disciplined reliability and governance while using low-code only when appropriate.

Design and implement a governed AWS data foundation: reference architecture, layered (medallion) data models, governance (PII, classification, lineage, residency), reusable engineering patterns for ingestion/transform/serve, loose coupling to operational systems, and occasionally build data products hands-on to accelerate delivery.