Top Data Jobs in Toronto

The Lead Data Engineer will design and enhance data pipelines and infrastructure for data-driven decisions, mentor engineers, and establish data governance.

Lead a team focused on data and ML systems at eBay, driving architectural decisions, product execution, and best practices in ML applications.

The Financial Data Operations Manager leads data operations teams, ensures SLA compliance, manages client relations, and oversees financial instrument data processing and governance.

The Lead Financial Data Analyst manages financial instrument data collection, quality control, client inquiries, and operational risk compliance, while coaching others and improving processes.

As a Senior Data Engineer, you will build a data platform, create data pipelines for market and trade data, and collaborate with traders and quants to enhance analytical workflows.

As a Lead Financial Data Analyst, you will manage the collection, control, analysis, and distribution of financial data for clients, ensure compliance with SLAs, and guide process improvements.

Founding Staff Data Scientist to build decision science from the ground up: design and deliver forecasting, optimization, experimentation, personalization, and recommendation systems; establish modeling standards, validation and deployment playbooks; mentor data scientists and integrate reusable decision workflows with the ML platform.

Lead a team to build scalable risk analytics, reporting, and data quality solutions. Translate business needs into technical analytics, ensure governance and regulatory compliance, engage stakeholders, and drive automation and continuous improvement of data processes and model monitoring.

As an Agentic Data Specialist, design and manage data systems for AI agents, ensuring seamless integration and collaboration across enterprise technologies.

Lead technical pre-sales engagements for commercial customers to design and deploy secure, scalable cloud database and analytics solutions. Build PoCs, run hackathons and architecture workshops, resolve technical blockers, and partner with engineering to accelerate migrations to Microsoft Fabric, Azure Databases and analytics services. Represent Microsoft in community forums and help customers modernize data platforms, governance, and AI-enabled analytics workflows.

Lead platform roadmaps for Matching, Activity Capture, and CRM integrations. Improve data quality, instrument metrics, triage customer issues, represent platform in customer and IT settings, and enable CS/SE teams while bringing AI tools into product workflows.

Design, build, and operate scalable AWS-based data infrastructure, pipelines, warehouses, and ETL processes. Maintain data quality, security, reliability, monitoring, and cost efficiency while supporting analytics, finance, dashboards, and reporting. Translate cross-functional requirements into durable data solutions, troubleshoot complex issues, manage Terraform infrastructure, and establish engineering standards. The role also mentors junior team members and improves data platform capabilities in a remote fintech environment.

Own the design, development, and operation of scalable AWS data infrastructure, including pipelines, ETL processes, data warehouses, data marts, and data quality systems. Build infrastructure with Terraform, optimize reliability and cost, support analytics and financial reporting, ensure security and compliance, troubleshoot complex issues, and mentor junior engineers. Collaborate with finance, product, analytics, and engineering teams in a remote fintech environment.

Own the AI and data product roadmap, prioritize and sequence development with engineering and founders, ship trustworthy AI experiences, drive adoption, define feature success metrics, evaluate outcomes, and manage the analytics foundation. The role requires technical fluency in AI, prior software engineering experience, production AI product delivery, strong product judgment, and comfort working in a fast-paced, remote startup environment.

Lead analysis of customer, digital, and transportation data to build targeting, pricing, and optimization models. Design and run customer analytics (price elasticity, churn, LTV, segmentation), mentor junior data scientists, lead stakeholder projects, and support data storage planning and cross-department representation to drive actionable, data-driven business decisions.

Lead design and delivery of scalable, governed data ingestion and integration pipelines for structured, semi-structured, and multimodal data to enable AI, ML, and Generative AI. Partner with product, data science, architecture, risk, and operations to define requirements, ensure data quality, lineage, security, and observability, and evaluate new technologies. Mentor engineers, drive cross-functional initiatives, improve platform resilience, and measure ingestion and AI data effectiveness.

Design, build and maintain Azure-based data ingestion, ETL and curation pipelines (batch and real-time). Collaborate with stakeholders, prototype solutions, ensure data quality and compliance, automate/monitor analytics services, provide L3 production support, and follow agile practices with code reviews and CI/CD.

Senior data specialist responsible for leading data mapping, documenting field-level mappings and lineage, ensuring data quality and validation, performing reconciliation, collaborating with engineers and stakeholders, and building reports/dashboards to support analytics and governance across Azure/Databricks enterprise data platforms.

Design, validate, and deploy publication-grade predictive and causal models for commercial and patient outcomes in rare disease. Build survival and time-series models, Bayesian marketing-mix and next-best-action systems, integrate Snowflake Cortex AI and AI agents, ensure HIPAA-compliant data operations, implement interpretability and bias guardrails, and translate insights to commercial stakeholders.

Lead a 20+ person Data & AI org owning strategy, platform, MLOps, applied AI productization, data governance, experimentation, and cross-functional partnership to drive AI-powered customer outcomes.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Leads architecture and development of large-scale batch and real-time data platforms and pipelines. Ensures data quality, governance, reliability, observability, scalability, and cost efficiency while building self-service capabilities. Supports analytics, machine learning, and generative AI teams with trusted data infrastructure. Establishes engineering standards, mentors engineers, and drives platform health across multiple teams. The role requires expertise in data integration, distributed processing, data modeling, cloud platforms, CI/CD, and infrastructure automation.

Build and operate production-grade data ingestion and transformation pipelines in Databricks using Spark/PySpark, SQL, Delta Lake, and medallion architecture. Manage schemas, data quality, deduplication, API integrations, orchestration, monitoring, alerting, recovery, and performance optimization. Collaborate with data scientists and application teams to provide reliable data for LLM/NLP pipelines. Contribute to CI/CD, documentation, data architecture, and onboarding new data sources.

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%.

Own the zero-to-one lifecycle for integrating structured financial data into AlphaSense. Translate investor research and capital-markets workflows into data requirements, architectures, and product solutions. Partner with engineering and platform teams across Web, Excel, and AI to deliver, test, launch, and enhance products. Conduct customer research, analyze competitors and industry trends, prioritize trade-offs, and align cross-functional stakeholders around product strategy and execution.