Top Data Jobs in Toronto

Lead data-driven underwriting improvements by analyzing claims, risk signals, and underwriting data; build statistical and ML models for cyber risk assessment, pricing, and automation; produce dashboards and reports; collaborate with actuarial, product, and engineering teams; mentor teammates and lead cross-functional initiatives to optimize underwriting and risk selection.

Design, build, and maintain scalable Databricks-based ETL/ELT pipelines and data models optimized for AI/ML. Manage orchestration, data quality, governance, and CI/CD (ADO → GitHub) while collaborating with cross-functional teams in Agile delivery.

The Senior Data Engineer will scale and evolve the data platform, develop ETL/ELT pipelines, establish best practices, and optimize systems for performance while collaborating with various teams.

Manage a team of cloud engineers and architects, oversee data delivery, lead technical discussions, and mentor team members while ensuring technical accountability on projects.

Lead and develop a team to govern and validate clinical data builds, vendor deliverables, and data integrations. Implement and maintain CDASH/SDTM/ADaM standards, validation libraries, and submission-ready processes. Oversee data ingestion into the Clinical Data Warehouse, drive automation (SAS, Python, AI), maintain SOPs/QMS, and partner with Biostatistics, Data Management, Standards, and CROs to ensure high-quality, analysis-ready datasets.

Lead optimization and scaling of Snowflake architecture, build resilient ELT/ETL and MLOps pipelines, implement data observability, automate workflows with AI-assisted tools, and design enterprise semantic models to support analytics and commercial data products.

Lead and manage client data and digital strategy projects end-to-end: plan and run projects, produce C-level deliverables, coach and evaluate teams, perform advanced data analysis, define client needs, pitch solutions, and develop business proposals to grow accounts.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Deliver front-office investment technology consulting: requirements, workflow design, system configuration, integration, testing, and handover. Provide subject-matter expertise on OMS/EMS/PMS platforms, data architecture, cloud integrations, and program governance to ensure timely, on-budget transformations.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

Design and deliver enterprise-scale EDW and BI solutions, ensuring performance and scalability, while mentoring teams and engaging stakeholders.

As a Data Architect, you will architect data solutions, ensure data integrity, collaborate with stakeholders, and mentor junior team members, driving data value and management strategies.

Perform product analytics by analyzing large datasets, running A/B tests, building dashboards, applying statistical methods and ML for forecasting and segmentation, and communicating insights to product, sales, and customers to drive adoption and product improvements.

As a Data Engineer, you'll design and maintain data pipelines, model data for analytics, ensure data quality, and collaborate with teams to meet data needs.

The Data Analyst will support business needs through technical problem-solving and data analysis, focusing on improving user experience and conversion rates in a B2B2C environment.

Lead the Data Modeling team, focusing on the development of reusable core data models and metrics. Collaborate with various teams to ensure data quality and reliability in analytics and AI/ML. Hands-on role requiring experience in data modeling, SQL, Python, and Spark.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Lead the data solutions strategy for PR&C PSP division, managing data initiatives, teams, and collaborating on analytics projects. Ensure compliance, governance, and optimize data services while communicating technical solutions to stakeholders.

Analyze player behavior and drive product innovation through data-driven insights. Collaborate with teams to design and evaluate experiments, maintaining data quality and delivering meaningful insights.

Build and automate large-scale ML data pipelines and tooling for autonomous trucking. Improve dataset curation, deduplication, labeling, evaluation, and metrics infrastructure. Collaborate with robotics, autonomy, and infra teams to support continuous learning, deployment, and scalable AI infrastructure for Kodiak's fleet.

As a Senior Data Analyst, you will shape performance measurement, support growth marketing through A/B testing and customer analysis, and develop data models.

Embed with a core business area to define problems, set metrics, run analyses and experiments, build reports, and surface actionable insights. Drive outcomes by recommending and implementing data-driven decisions across growth, product, AI/automation, or partnerships, and communicate findings to stakeholders and leadership.

Embedded within a core business area, you will define analytical problems, build metrics and reports, run analyses and experiments, surface insights, and drive data-driven recommendations end-to-end to influence product, growth, AI, or partnerships.

Embed on a core business team to define problems, build metrics, run analyses and experiments, and drive data-informed product, growth, AI, or partnership decisions end-to-end. Surface insights, recommend actions, and own execution to improve key business metrics.

Embed within a core business area to own analytics end-to-end: define problems, build metrics/reports/experiments, analyze data, surface insights, and drive outcome-focused recommendations with stakeholders to improve growth, product, AI/automation, or partnerships.