Top Data Jobs in Toronto

Embedded in a core business area, own end-to-end analytics: define problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions to improve product, growth, AI/automation, or partnerships.

Embedded data scientist owning analytics end-to-end for a business unit: define problems and metrics, pull and analyze data, design experiments, build reports, surface insights, and drive recommendations that influence product, growth, and strategic decisions.

Embedded within a core business area, define analytics problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end with stakeholders.

Embed on a core business team to define problems, build metrics/reports, run analyses and experiments, and drive data-informed recommendations end-to-end to improve growth, product, AI/automation, or partnerships.

Embed within a core business area to define problems, build metrics, run analyses and experiments, create reports, surface insights, and drive data-informed product, growth, AI, or partnership outcomes end-to-end.

Analyze microbiome and longitudinal clinical data, develop biomarkers and microbiome metrics using statistical and machine learning methods, and create visualizations, reports, protocols, and study reports. The role supports research, product development, scientific publications, proposals, and clinical studies while collaborating independently with microbiome experts in a remote-first startup environment.

Lead clinical data management from study start-up to database lock, oversee DM deliverables and teams, liaise with sponsors, manage timelines/budgets/scope, mentor staff, ensure GCP/SOP compliance, coordinate cross-functional biometrics activities, support audits, and drive process improvements and client satisfaction.

As a Data Analyst, you will support data migration initiatives by analyzing data structures, mapping source to target systems, and collaborating with stakeholders to document requirements and findings.

The Data Steward ensures data governance and quality by managing metadata, supporting data quality initiatives, and collaborating with stakeholders to document data lineage and manage access controls.

Design and optimize scalable data pipelines, develop reusable frameworks, troubleshoot production issues, and enforce data engineering standards while collaborating with stakeholders.

The Data Operations Specialist will manage data operations, ensuring quality data services and compliance, while optimizing data workflows and conducting audits.

The Senior Pyspark Data Engineer will manage large-scale data processing systems, conduct feasibility studies, provide user support, and develop security measures while acting as a subject matter expert in applications development.

This role involves designing and developing scalable data solutions, automating tasks, and collaborating with teams to deliver business value. Responsibilities include strong database development using SQL and Python, as well as leading innovative projects and ensuring compliance.

Seeking a Senior Data Scientist to lead data science initiatives, drive machine learning strategies, implement data solutions, and collaborate with cross-functional teams to enhance programmatic efficiency.

As a Senior Data Engineer at Nimble, you will manage the end-to-end data platform, build pipelines, enhance data modeling, collaborate with teams, ensure data security, and mentor others.

The Account Executive will develop business with enterprise customers, manage sales processes, maintain customer relationships, and present proposals while driving revenue with strategic customers across various regions.

Lead and/or staff-level technical roles to design, build, and operate petabyte-scale batch and streaming data pipelines, APIs, and analytical platforms. Responsibilities include platform architecture, data modeling, query and API design, reliability, observability, performance optimization, cross-team coordination, and mentoring engineers through a transformation to workload-aware, near real-time data systems.

The Manager of Data Management leads a team, oversees data processes, ensures data integrity, collaborates with IT, and supports compliance for the organization's data management systems.

As a Data Engineer, you will design and implement solutions for data integration and manage data quality, while collaborating with various teams for optimal data usage and decision-making.

As a Senior Engineering Manager in Data Engineering, you'll lead a team to build a Data Platform, improve engineering practices, and ensure project delivery through effective collaboration and system architecture design.

Lead product-focused analytics for seller and listing performance: define KPIs, build/maintain data models and ELT pipelines, run experiments and impact measurement, deliver dashboards and automated reporting, use AI-assisted tools and analytics agents, and mentor junior analysts to drive business outcomes.

As a Senior Data Scientist, you will design, build, and operate machine learning and GenAI systems, influence technical direction, and mentor junior data scientists, focusing on production ML systems.

Lead high-impact AI initiatives, develop ML products, collaborate with cross-functional teams, and mentor data scientists in a dynamic environment.

Lead and build a hands-on data science and analytics function for a live-service gaming company. Own analysis (SQL, Python/R), dashboards, KPIs, experimentation, data quality, pipelines, and executive reporting. Partner across product, ops, marketing, finance and engineering. Implement AI-driven analytics, automation, and copilots, then hire and scale a high-performing team while establishing governance and responsible AI practices.

Own strategy, roadmap, and delivery of analytical data products. Lead client discovery, prioritize backlogs, run Agile ceremonies, and ensure launches. Collaborate with engineering and data teams on data quality, governance, architecture (Snowflake, BigQuery, ClickHouse, Databricks, Redshift), perform SQL validation, and drive product adoption and continuous improvement.