Top Data Jobs in Toronto

Analyze user behavior and product performance, create reports and dashboards, and provide data-driven insights to teams for decision-making.

Cygnus seeks Data Scientists and Analytics Developers committed to customer success, emphasizing ownership and a strong value system.

Lead and mentor a team of developers, oversee cloud product delivery, drive engineering practices, and ensure high-quality standards while contributing to cross-functional initiatives.

Lead and manage a team to develop and maintain interactive dashboards and visualizations, ensuring quality delivery and data integration.

Design, implement, and manage Azure cloud solutions with a focus on automation. Develop CI/CD pipelines, ensure security compliance, and mentor IT team members.

Lead PICTON’s enterprise data strategy, architecture, governance, and engineering capabilities across investment data, analytics, and AI. Build scalable Databricks and Snowflake platforms, reliable data pipelines, financial data models, semantic layers, APIs, and governance standards. Partner with Engineering, Investment, Product, and AI leaders while building and managing a high-performing data platform team. Translate complex financial data into reliable products that support portfolio construction, risk, performance, analytics, applications, and AI workloads.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, testing, observability, governance, and quality practices. Partners with stakeholders to deliver data-driven solutions, supports incident response, and provides technical leadership through design reviews, mentorship, hiring, and team development.

Lead design, architecture, and delivery of enterprise-scale data platforms using Databricks and Spark. Architect Lakehouse solutions, build ETL/ELT pipelines, implement IaC and CI/CD, ensure data governance and security, mentor engineers, and partner with Fortune 500 clients from discovery through production.

Design, build, deploy, and own high-performance, scalable data-platform components for streaming analytics, reporting, and ML. Collaborate with cross-functional teams, perform design/code reviews and performance tuning, debug production incidents, participate in on-call rotation, and mentor engineers to scale the organization.

Build, maintain, and optimize data pipelines and transformations on Snowflake and Azure. Develop DBT models, ingest diverse data sources, monitor pipelines, troubleshoot issues, collaborate on governance, and use GenAI-assisted tools to accelerate development and documentation.

Lead design, development, deployment, and operational support of scalable cloud data platforms and ELT/ETL pipelines on Azure. Build data products for analytics and AI, optimize performance/cost, implement CI/CD and observability, collaborate with stakeholders, and troubleshoot complex data workflows using Snowflake, dbt, Python/PySpark, and orchestration tools.

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.

As Senior Product Manager, you'll manage the product roadmap, engaging with healthcare professionals and driving cross-functional execution in a high-growth tech environment.

Analyze complex structured and unstructured datasets, develop predictive models, apply statistical and machine learning methods, identify data quality issues, and generate actionable insights. The role integrates data from multiple sources, supports modernization and governance initiatives, creates dashboards and analytical frameworks, and communicates findings to technical and non-technical stakeholders. It is hybrid in Toronto with onsite work required five days as project needs dictate.

Build and improve scalable full-stack products for higher education, owning initiatives across the software development lifecycle from discovery through release. Collaborate with Product, Design, and Engineering in a cross-functional Scrum team, experiment with AI technologies, resolve bugs, complete tickets, and deliver reliable, tested, user-focused features.

Manage interfaces and eligibility files for group insurance: perform daily data loading, validation and updates, document procedures, communicate with clients and stakeholders, and recommend process improvements and automation.

Design, develop, and own scalable real-time and batch data pipelines using Kafka, Spark, Flink, Airflow, Databricks, and AWS. Implement data quality frameworks, oversee projects from design through long-term support, identify scalability risks, document systems, and collaborate with data science, product, and engineering teams to guide technical roadmaps.

Leads Claims Data & Analytics strategy, identifying trends, interpreting claims results, and developing tools and models to improve operational efficiency and indemnity control. Advises senior executives through quantitative analysis and recommendations, manages resources and strategic initiatives, and oversees approximately 15 analysts through three direct reports. The role also drives talent development, stakeholder engagement, organizational capability building, and collaboration across business units and countries.

Develop and scale customer-facing analytics, dashboards, reports, and data products for a healthcare AI platform. Partner with Product, Engineering, Sales, BizOps, customers, and other stakeholders to translate needs into data requirements and actionable insights. Lead reporting initiatives, resolve customer reporting issues, support high-stakes customer interactions, improve internal workflows, and help productize operational and clinical data into differentiated customer value.

The Database Research Expert will lead the architecture design for a data analytics platform, optimize database performance, and guide research on innovative database technologies.

The role involves analyzing software requirements, contributing to software development, mentoring developers, and leading design/code activities in a big data context.

Research emerging trends in AI-powered big data systems, develop proofs-of-concept, optimize performance, and collaborate on innovative solutions.

The Technical VP for AI & Data Storage will define technology roadmaps, lead research advancements, and mentor engineers in evolving storage systems integrated with AI.

The Technical VP will lead innovations in big data and AI strategies, optimize data performance, manage R&D teams, and engage with industry communities.

Conduct applied research to develop AI data engine and storage architectures for large-scale workloads, optimize system software, and advance AI storage technologies.