Top Data Jobs in Toronto

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.

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 with a business unit to define problems, build metrics and analyses, run experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Embedded on a core business team, the Data Scientist defines problems, builds metrics and reports, runs analyses and experiments, and translates insights into executable recommendations to drive growth, product, AI/automation, or partnership outcomes.

Embed within a business unit to define problems, build metrics/reports/experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Embedded on a core business team, you will define problems, build metrics/reports, run analyses and experiments, and drive data-driven recommendations end-to-end. You will partner with product, growth, finance, and operations to surface insights and influence leadership decisions.

Develop testing and automation software tools, create and maintain system tests, and execute testing plans in collaboration with developers.

Design and manage data platforms and pipelines, optimize workflows, ensure data security and compliance, perform data modeling, and collaborate with stakeholders.

The Data Designer III role involves data engineering, managing data pipelines, communicating with stakeholders, leading data governance, and ensuring quality data management practices.

As a Data Designer II, you will guide data management initiatives, implement data quality processes, support data lifecycle, and collaborate with business and technology teams for effective data solutions.

As a Data Annotation Specialist, you will label, audit, and optimize data for AI models in Chinese while ensuring high accuracy and quality. You will perform repetitive tasks and collaborate with teams, providing feedback and recommendations for data improvement.

As a Software Engineer, you will create tools and pipelines for AI development, manage data solutions for ML teams, and ensure high-quality code.

Execute design and operating effectiveness testing for data and AI governance controls, perform walkthroughs and artifact analysis, identify and document control issues, and provide risk-based advisory support to business, technology, and risk partners to improve control design and governance practices.

Design and build scalable ETL data pipelines and RESTful APIs/microservices. Improve performance, monitor and troubleshoot data flows, ensure data security/compliance, write tests, and collaborate cross-functionally.

Design, develop, test and maintain cloud-based data applications and infrastructure on AWS. Build and optimize ETL and streaming pipelines, support CI/CD and infrastructure as code, and collaborate with stakeholders to improve performance and deliver technical solutions independently.

Lead a team of analysts to deliver enterprise analytics and revenue reporting, ensure data accuracy and governance, manage data repositories and pipelines, create dashboards and executive reports, validate models and processes, and translate complex data into actionable business insights.

Lead implementation of data management, retention, disposition, and governance for TD Securities. Define retention requirements, enable retention metadata, support legal holds and disposition workflows, drive automation (metadata, lineage, quality), advise on data practices in projects, manage risks and issue remediation, monitor metrics and controls, and coordinate stakeholders across Technology, Risk, Legal/Compliance, and Records Management.

Design and build reusable, metadata-driven data and AI engineering frameworks using Databricks and cloud-native services. Architect and deploy Delta Live Tables, Lakeflow, and orchestrated Databricks workflows for automated data processing and AI agent deployment. Implement RAG/Graph RAG solutions and agent orchestration (AgentOps, Agent-bricks, LangGraph), integrate real-time streams, manage AI agent lifecycles, and ensure governance via Unity Catalog or AWS Glue.

Design and run experiments, apply statistical analysis and ML (including GenAI/LLMs) to product and business problems, build and deploy predictive models, collaborate with product/marketing stakeholders, ensure data quality and governance, and communicate actionable insights to influence strategy.

Design, build, and maintain Databricks/Spark ETL pipelines and Bronze-Silver-Gold architectures; implement event-driven streaming with Pub/Sub and Protocol Buffers; prepare datasets for LLM fine-tuning and embeddings; integrate third-party sources (Shopify, Klaviyo); implement attribution, CLV, and analytics models; ensure data quality, monitoring, and idempotent retry-safe pipelines.

Lead cross-functional execution for data labeling, data capture, and hardware deployments. Scope ambiguous problems, create execution plans, manage timelines, risks, and stakeholders, communicate status to internal teams and customers, and improve operational processes and documentation.

As an Entry Level Data Entry Clerk, you will ensure accurate data entry, organize records, and collaborate with the team while working remotely.

As a Data Entry Assistant, you will transfer information into the database, verify data accuracy, and assist in creating reports and spreadsheets.

Lead the development of data products at Procore, leveraging construction data to create predictive models and insights. Collaborate across teams to define product strategy, data quality standards, and success metrics, while utilizing AI tools effectively.

The role involves owning product feature development, participating in system design, and maintaining software performance in an agile setting.