Top Data Jobs in Toronto

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

The Enterprise Solutions Specialist drives client success through project management and operational oversight, ensuring scalable processes and effective communication between teams. This role involves direct client engagement and supports automation initiatives.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

Develop, evaluate, and deploy machine learning models for risk scoring, threat intelligence, and vendor risk assessment. Perform exploratory data analysis on large datasets, validate models for accuracy and scalability, and integrate models into production with engineering and product teams. Communicate findings to technical and non-technical stakeholders and stay current on research and methods.

Lead design and delivery of cloud-based data architectures and ETL/ELT pipelines for marketing analytics. Advise clients, integrate marketing data, build and optimize pipelines and reusable models, ensure data quality/security, mentor team members, and align technical solutions to business goals.

Serve as delivery lead and senior digital analyst for clients: gather measurement requirements, analyze large event datasets (BigQuery/SQL), build visualizations, present insights, guide implementations (GTM/Tag tools), coordinate cross-functional delivery, and ensure QA and on-time execution.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Lead finance data stewardship across D365, OneStream, Longview and the enterprise data lake. Implement governance policies, manage metadata and lineage, monitor data quality with Informatica, triage and remediate issues, enable AI-readiness, partner with Finance/IT/Data teams, and produce audit-ready reports and dashboards.

Responsible for designing and operating data architecture, building ETL/ELT pipelines, implementing data quality frameworks, and collaborating on ML initiatives.

Lead enterprise data strategy, governance, and analytics to turn data into actionable insights. Partner across IT, privacy, finance, operations and charity functions to implement data platforms, reporting tools, AI best practices, and data products. Build data literacy, mentor analytics staff, manage vendors, and support evidence-informed decision-making and impact measurement.

Lead enterprise data strategy, governance, platforms, analytics, and data product development. Advise leadership on AI, privacy, and data-driven decision-making. Partner across IT, Finance, Operations, and external vendors to deploy data solutions, build data literacy, mentor analytics staff, and measure organizational impact to inform investments and improve services for children and communities.

Lead administration and roadmap for BI tools (Domo, Tableau); build advanced dashboards with custom code; automate ETL with Airflow and Jenkins; use SQL, Python, and Java; apply AI to speed development; train and mentor analysts and ensure high-quality, user-focused data visualizations.

Lead strategy and research for Generative AI and Graph ML, drive end-to-end ML system design and production deployments, mentor scientists, align ML innovation with product goals, and represent the company externally.

The Senior Analyst, Growth Analytics (CRM) will own CRM analytics strategy, build measurement frameworks, analyze experiments, track performance, and present insights to leadership to optimize customer retention and lifetime value.

Lead and mentor a data science team to enhance the Rider App experience through machine learning, analytics, and experimentation. Partner with stakeholders to drive data-driven decisions and innovation.

Senior Analyst supporting finance technology transformation: research and implement AI/ML and automation solutions, deliver technology projects, improve finance processes and compliance, support audit/SOX readiness, produce analysis and stakeholder communications, and collaborate across Finance, IT, and Operations to drive measurable efficiency and cost savings.

The Data Engineer will provide technical support and deployment expertise, review data designs, and ensure code quality while collaborating with international teams on a strategic data initiative.

Lead growth-focused data science across cross-functional squads: design experiments, build models and dashboards, run causal analysis and forecasting, define KPIs, identify high-leverage opportunities, and drive data-informed product and marketing decisions to improve acquisition, retention, and monetization.

Lead growth-focused data science within a cross-functional squad: define KPIs, run experiments, perform causal inference and forecasting, build dashboards and self-serve tools, identify high-leverage opportunities across acquisition, activation, retention and monetization, and evangelize data-driven decision making to drive measurable business impact.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

Lead development and refactor optimization algorithms in Python for pricing and supply‑chain problems. Design and implement predictive models and mathematical optimization solutions, collaborate with cross‑functional teams, coach data scientists, and translate business requirements into data‑driven pricing and replenishment strategies.

Design, build, and maintain reliable data pipelines and lakehouse assets for batch and streaming workloads. Implement ETL/ELT in Python and SQL, orchestration and monitoring (Airflow/Kubernetes), and retrieval/RAG/vector search pipelines for LLM and AI applications. Ensure data quality, lineage, versioning (Delta/Parquet/Iceberg), cost/performance optimization, and collaborate with MLOps and sports data teams.

Design and implement real-time MAC data-plane software for high-throughput satellite links. Build packet-processing pipelines, queue and buffer management, retransmission and loss recovery, and integrate with PHY, modem, FPGA, DMA, and SoC hardware. Optimize for low latency and high throughput using zero-copy, scatter-gather, lock-free structures, and hardware offload. Lead architecture through prototyping, hardware integration, performance qualification, and operational deployment while diagnosing timing and synchronization issues.

Lead development and evolution of AlphaSense's financial data taxonomy and generation rules. Serve as subject-matter expert, align cross-functional teams, define requirements for new datasets, ensure AI/LLM compatibility, maintain documentation, and build scalable frameworks to expand and maintain high-quality structured financial data for capital markets users.

Design and implement automated data quality frameworks and tests for BigQuery-based pipelines. Collaborate with data engineers and product to define data contracts and quality gates, build observability and anomaly detection, validate complex ELT/ETL processes, and integrate data tests into CI/CD workflows. Drive improvements in documentation, governance, and pipeline reliability.