Top Data Jobs in Toronto

Partner with business, IT, and analytics teams to define and enforce data standards, improve data quality, investigate data issues, and govern large datasets. Use SQL and BI tools and data catalogues to document metadata, shape enterprise-wide governance practices, and influence stakeholders to adopt consistent data management practices.

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.

Lead implementation projects focused on data management and integration for financial institutions. Define solutions using SimCorp One®, liaise with clients, coordinate with project managers, mentor colleagues, improve implementation best practices, and ensure delivery of data integration and performance improvements.

Design, build, and operate large-scale security data pipelines, lake/lakehouse storage, and analytics to normalize telemetry from a massive GPU fleet. Ensure data platform security (encryption, RBAC/ABAC, audit), enable trustworthy analytics and detection, collaborate across teams to define data contracts, and maintain data quality, provenance, and freshness for security posture and automated response.

Design, build, and maintain scalable data pipelines, optimize Databricks/Spark and Delta Lake architectures, deliver trusted datasets for analytics and ML, implement observability and data quality, and drive platform architecture and team mentorship.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

The Product Data Analyst will analyze operational data, develop analytics frameworks, create dashboards in Tableau, ensure data integrity, and collaborate across teams to enhance decision-making and reporting.

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.

Lead data architecture efforts: design relational and physical data models, develop and maintain ETL and reporting solutions, ensure data quality and governance, monitor data loads, support database performance and security, and translate business requirements into data designs.

Lead full-cycle enterprise sales for data and intelligence consulting: originate pipeline, run discovery, manage complex multi-stakeholder deals, create SOWs with delivery teams, co-sell with partners, and meet revenue targets while leveraging AI tools.

As a Staff Software Engineer, you'll design and deliver a core data platform, focusing on architecture, pipeline components, and AI integration. This hands-on role emphasizes creating production code and establishing technical standards within a small team.

Lead research and development of fuel detection and wildfire-risk models using satellite, LiDAR, and environmental data. Design validation, uncertainty quantification, and ground-truth strategies. Prototype ML approaches and partner with engineers to productionize models. Integrate fire science with data-driven methods, define scientific standards, communicate results, and mentor engineers.

Design, build, and optimize Microsoft Fabric-based ingestion, transformation, and curated data layers. Migrate legacy sources, implement business rules, develop API-driven workflows, ensure data quality, security, and monitoring, and support CI/CD and operational delivery for analytics and reporting.

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.

Lead and scale a product-focused data science organization to build production ML systems for personalization, recommendation, experimentation, and causal inference. Define strategy, partner with Product and Engineering, own end-to-end ML lifecycle, mentor the team, and measure product impact through robust analytics and monitoring.

Embed as financeAI partner across Tax, Controllership, Treasury, FP&A, and Opco teams to identify, prioritize, prototype, and deliver AI and data science solutions. Translate finance problems into technical requirements, ship LLM/retrieval/agentic tools, drive adoption, measure ROI and impact, and help scale the AI finance capability and team.

Design and develop data products for commercial insurance by analyzing new data sources, elaborating requirements and user stories, supporting project scoping and testing, ensuring data documentation, integrity monitoring, and governance, and collaborating with business and engineering teams to validate solutions.

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.

As an Engagement Manager, lead BI projects, guide teams to transform data into actionable insights, and develop BI strategies ensuring data integrity and quality.

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.

Design, develop and maintain data controls and governance for Financial Crime & Risk Management systems. Lead complex data initiatives, perform data profiling, root cause analysis, troubleshoot data pipelines, automate processes, integrate databases with analytics platforms, and collaborate globally to ensure data integrity for payments and transaction monitoring.

As a Data Architect, you'll design scalable data solutions, collaborate with teams to drive AI and data science projects, and communicate insights effectively.