Top Data Jobs in Toronto

Lead ERP data conversion activities: extract, cleanse, transform, validate and load master, reference, and transactional data. Build and optimize ETL pipelines, mappings, validation routines and load files. Support mock conversions, cutover rehearsals, reconciliation, error remediation, data quality metrics, and documentation while working with SMEs and ERP teams.

Perform product-focused analyses and A/B tests to generate actionable insights; build dashboards and KPIs; ensure data quality; synthesize diverse data sources; present findings to stakeholders and senior leadership to guide product and business decisions.

Lead a team to define and implement enterprise data quality strategies, design and run automated data quality and regression tests, validate end-to-end ETL/ELT pipelines, perform source-to-target reconciliation and data profiling, collaborate with stakeholders, and establish data quality metrics and governance to ensure reliable reporting and analytics.

Evaluate and rank multimodal model outputs, stress-test and document failures, author and edit dataset prompts/responses, build and apply rubrics, perform high-quality annotations across text, image, and structured formats, participate in calibration checks, adapt to evolving tasks, and report model performance trends to cross-functional teams.

Implement and configure enterprise Workday HCM solutions, lead the data workstream including gathering, transforming and loading organizational and employee data, liaise with customers and internal stakeholders, deliver data solutions, support sales and practice improvements, and educate others within the Workday practice.

Lead engineering and deliver ML model pipelines and API-based analytical solutions. Design scalable, secure, maintainable data integrations, enforce MLOps best practices, coach technical team members, and collaborate with BI architects to meet business objectives using insurance PolicyCenter data.

Lead advanced analytics projects to build, validate and monitor predictive and descriptive models. Determine technical approaches, evaluate diverse data sources, translate findings for stakeholders, and provide technical leadership and mentorship to analytics teams to support business decisions and risk mitigation.

Analyze large datasets to produce dashboards, metrics, and insights supporting product, care, and business decisions. Build BI infrastructure, run A/B and multivariate tests, perform ad-hoc analyses and visualizations, and collaborate with stakeholders and data scientists to inform strategy and operations.

Lead enterprise data architecture for wholesale lending: develop conceptual, logical and physical data models, information architecture, data integrations, governance, lineage, and metadata. Collaborate with stakeholders, federated architecture teams, and junior staff to design scalable, performant solutions aligned to business strategy.

Design, build, test, and support data and AI products by developing ETL/ELT pipelines, data models, and automation. Partner with stakeholders to translate requirements, perform data profiling and regression testing, troubleshoot production issues, and contribute to technical designs and process improvements to accelerate delivery.

Design, build, test, and support data and AI products. Translate business requirements into data solutions, contribute to technical design, develop reusable frameworks and automation, perform ETL/ELT development and testing, conduct regression testing and root-cause analysis, and drive process improvements for delivery and deployment.

The Software Engineer for Data Infrastructure & Acquisition will manage data collection for model training, optimize ingestion pipelines on GCP, and collaborate with the AI team to enhance data quality and scalability.

The IT Data Specialist provides technical leadership in data analysis including data modeling, ETL, reporting design, and ensures data quality management, collaborating with business and technology partners.

The Data Engineer II will perform data engineering functions such as data modeling, ETL, and data quality management while collaborating with various teams and maintaining compliance with data standards.

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.

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

Design and develop data products for Commercial Insurance by converting business needs into user stories and technical specs. Evaluate data sources, support Agile development, scope projects, plan and review tests, ensure data documentation, governance, and integrity, and share analytics best practices with stakeholders.

Owner of Float's Snowflake architecture and ingestion layer: build orchestration, observability, data quality and contracts, CI/CD for data code, support ML pipelines, and collaborate with analytics, data science and product teams to deliver reliable, cost-efficient, governed data.

Drive analytics and measurement for AI product adoption and impact. Build agentic analytics, small language models, predictive models, and customer-facing insights. Design and run experiments (A/B tests, causal inference), ship production models, and translate ambiguous business questions into actionable recommendations used by product, sales, and leadership.

The Big Data and SQL Developer is responsible for designing and implementing scalable Big Data solutions, mentoring engineers, and engaging with stakeholders, balancing technical tasks with leadership duties.

Develop, fine-tune, and deploy generative AI and machine learning models (including prompt engineering and RAG). Clean and analyze large datasets, run experiments, collaborate with cross-functional teams, and deploy models on Azure/Databricks while communicating results to technical and non-technical stakeholders.

Review merchant source documents, validate and correct input/output data and batching errors, verify control totals and reports, support daily operations (profile creation, device/SIM activations, rollouts), provide first-level support, coordinate urgent requests, collaborate cross-functionally on escalations, and identify process improvement opportunities.

Lead a data science team within Corporate Treasury to build liquidity and funding risk models, dashboards, and analytics. Hands-on work with large datasets, ML/deep learning models, PySpark/Python and big-data platforms, delivering reports and recommendations to business stakeholders while coaching and developing team members.

Lead and coach a data platform governance team within the Chief Data and AI Office. Manage production support, application delivery, solution design, vendor governance, and stakeholder relationships. Drive operational standards, project delivery on time and budget, and cross-functional alignment while promoting innovation and change adoption.

Manage end-to-end data migrations for new client implementations: extract, cleanse, map, validate, and import data. Work with implementation teams and clients to resolve data quality issues, document processes, support testing and go-live, and continuously improve migration practices.