Top Data Jobs in Toronto

Support and improve the enterprise data governance program by establishing policies, standards, controls, stewardship practices, metadata, data quality requirements, and governance metrics. Administer Microsoft Purview, including data cataloging, lineage, classification, and business glossary capabilities. Collaborate with stakeholders on compliance, risk, audits, information security, training, and remediation while promoting effective and compliant use of organizational data.

Designs and optimizes Azure SQL databases, data warehouses, ETL pipelines, data models, and Lakehouse solutions for datasets exceeding 5TB. Develops complex SQL queries, stored procedures, functions, and triggers; performs performance tuning and troubleshooting; and supports Azure infrastructure. The role also contributes to technology roadmaps, leads complex backend projects, conducts code reviews, and mentors developers while using tools including PySpark, Azure Data Factory, Databricks, and Delta tables.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Gather, process, analyze, and visualize data for personal finance and business journalism. Collaborate with reporters, editors, and visual journalists on consumer-focused data stories, verify newsroom data work, build reporting tools, and promote data literacy. The role requires strong news judgment, data analysis and visualization skills, dataset management experience, and familiarity with Python or R. Additional assets include AI, APIs, database management, AWS, and business datasets.

Analyzes warehouse, distribution, inventory, and field service data to improve logistics performance. Develops Power BI dashboards and KPI reporting, manages inventory accuracy and cycle-count programs, evaluates replenishment and truck-stock performance, investigates variances, and recommends operational improvements. Supports Lean and 5S initiatives, reporting automation, data quality, and cross-functional supply chain decisions involving logistics, procurement, supply planning, and service operations.

Leads an analytics team responsible for building governed data products, modernizing reporting, standardizing enterprise KPIs, and delivering self-service analytics and executive-ready insights. The role guides data strategy and architecture, manages governance and metadata, partners with business and IT stakeholders, maintains operational reporting, and mentors analysts and developers while using agile delivery practices.

Designs and trains computer vision models for vehicle damage detection, builds high-throughput containerized model-serving microservices, and manages the end-to-end ML lifecycle. The role includes developing Kafka-based streaming data pipelines, optimizing models for real-time inference, maintaining annotation and evaluation frameworks, and collaborating with stakeholders to deliver scalable production solutions.

Develops end-to-end computer vision and machine learning solutions for vehicle damage detection. Responsibilities include training robust models, building high-throughput containerized REST/gRPC model-serving microservices, managing the full ML lifecycle, optimizing real-time inference, collaborating with stakeholders, and maintaining Kafka-based streaming data pipelines. The role also involves annotation and evaluation frameworks, cloud backend engineering, and technical design leadership.

Senior software developer responsible for building and operating scalable backend services for a VIN Decoder platform. The role develops Python APIs, services, and workers using PostgreSQL, Redis, AWS, and GCP BigQuery; designs data models and optimized SQL; integrates third-party APIs; and drives reliability, observability, testing, incident response, and operational maturity. The developer also contributes to architecture, code reviews, documentation, Agile delivery, on-call support, and technical mentorship.

Leads Data Quality Analysts supporting AI model evaluation and improvement. Owns quality across workflows through audits, calibrations, feedback loops, trend analysis, and stakeholder alignment. Translates evolving guidelines into team direction, manages operational performance, supports hiring, drives process and tooling improvements, handles complex decisions, and coaches team members. The role balances quality, delivery, resource management, and continuous improvement in a human-in-the-loop AI environment.

Builds and improves reporting dashboards, data visualizations, and self-service analytics tools. Extracts, cleans, aggregates, and analyzes data using SQL and related technologies to deliver actionable insights. Translates business needs into technical specifications, documents data processes, supports repeatable analytics solutions, and trains non-technical users on reporting tools. Collaborates with stakeholders to optimize analytics systems and inform business decisions.

Supports clinical study teams by programming and validating SDTM and ADaM datasets, tables, listings, figures, visualizations, and dashboards. Transforms statistical analysis plans into traceable deliverables, contributes to exploratory and biomarker analyses, performs quality control, resolves technical issues, and supports regulatory submissions. Collaborates with senior programmers while developing expertise in clinical research, CDISC standards, statistical programming, data integrity, and reproducible analytics.

Lead data science initiatives supporting Wealthsimple’s Commercial team and AI Voice Agent program. Build reliable data models, develop and maintain machine-learning models, design experiments, conduct analyses, and create dashboards and metrics. Partner with Sales, Operations, Product, Design, Engineering, and leadership to improve sales processes and business outcomes. Mentor teammates and communicate insights to senior stakeholders.

Lead design, development, and deployment of data science and GenAI solutions. Translate business goals into ML problems, engineer features, train/evaluate models and LLMs, build RAG/agent systems, work with vector DBs, mentor juniors, and communicate results to stakeholders.

Leads end-to-end analytics for marketing, digital, client, advisor, and sales data. Defines business questions, integrates data, develops dashboards and reporting, automates recurring processes, identifies trends and performance opportunities, and translates findings into recommendations. Presents insights to technical and non-technical stakeholders while supporting the transition from SAS to cloud-based Python and PySpark analytics.

Design, build, deploy, and maintain scalable multi-source ELT pipelines and enterprise data warehouse infrastructure. The role involves optimizing data systems for cost, performance, reliability, and SLAs; curating campaign and business data; creating monitoring and automation; and collaborating with engineering, analytics, SRE, and platform teams to deliver reliable data products.

Implements and configures an AI platform by integrating customer data, building ETL workflows, troubleshooting pipelines, managing recurring data deliveries, and automating manual processes. The role collaborates with customers and internal teams to define technical requirements, resolve implementation issues, deploy tailored solutions, and maintain data security and privacy.

Own complex customer data implementations from technical discovery through production go-live. Build automated ETL workflows, scalable data ingestion solutions, validation frameworks, and data-quality processes using Python, SQL, PySpark, Snowflake, and AWS. Troubleshoot integrations and production issues, ensure compliance with security and privacy requirements, mentor junior engineers, and improve implementation standards and tooling while working directly with technical and non-technical customer stakeholders.

Leads data governance and stewardship for the wealth management business. Owns data quality, integrity, metadata, business glossaries, lineage, classification, retention, and critical data elements. Partners with business, technology, compliance, and operations teams to resolve data issues, improve processes, and ensure consistent practices. Supports regulatory, privacy, risk, and Canadian wealth management requirements while managing the business data model end to end.

Own end-to-end analytical projects supporting commercial, product, marketing, and SEO decisions across the Sports business. Build forecasting models, improve Tableau dashboards, apply statistical and behavioral analysis, strengthen data quality and pipelines, and identify growth opportunities. The role also involves stakeholder management, translating business needs into analytical solutions, influencing strategy, and mentoring team members.

Sets the technical vision for data science, leads complex credit risk, pricing, and loss forecasting models from conception through production, and architects scalable machine learning pipelines. Partners with senior stakeholders to shape strategy, establishes modeling and validation standards, mentors technical staff, and communicates recommendations to executives. The role requires deep fintech lending experience, advanced statistical and machine learning expertise, and hands-on production model deployment.

Facilitates enterprise customer data ownership and governance across Canadian lines of business. Coordinates data standards, definitions, quality rules, issue remediation, downstream data consumption, and governance forums. Partners with business, technology, risk, operations, source systems, and data consumers to align customer master data requirements, resolve cross-segment dependencies, and promote consistent adoption of the enterprise customer master platform.

Leads enterprise data design and governance across insurance domains, defining conceptual, logical, and physical models, data standards, contracts, metadata, lineage, and privacy controls. Partners with business, architecture, engineering, analytics, and AI teams to deliver scalable data solutions across warehouses, lakes, APIs, and operational platforms. Applies Property and Casualty insurance expertise and ACORD standards while guiding migration, modernization, AI-ready data, model lifecycle management, observability, and cross-domain governance.

Own the vision, roadmap, and lifecycle of Autodesk’s analytics data platform, including storage, compute, orchestration, reliability, security, compliance, cost, and performance. Partner with distributed engineering teams and technical users to deliver self-service infrastructure, lead migrations, prioritize capabilities, assess vendors, and measure platform adoption and business impact.

Develop analytical solutions, enterprise dashboards, reports, and data visualizations for Small Business Banking stakeholders. Partner with data engineering and technology teams to define data requirements, business rules, quality standards, and enhancements. Use Power BI, Tableau, Python, PySpark, Databricks, and Azure to support self-service insights and data preparation. Maintain governance documentation and controls while delivering accurate results in a fast-paced environment.