Top Data Jobs in Toronto

Executive leader responsible for the strategy, roadmap, product ownership, adoption, enablement, support model, operational excellence, governance, and financial management of an enterprise data and analytics platform. The role drives platform modernization, self-service, AI/ML readiness, service stability, compliance, cost optimization, stakeholder engagement, and measurable business value across the organization.

Research, design, and prototype next-generation data systems for AI applications (LLMs, agents, RAG). Build scalable architectures for vector search, semantic retrieval, knowledge management, and AI data lifecycle. Evaluate state-of-the-art vector/graph databases and distributed storage, develop proofs-of-concept and production components, and publish patents and papers.

Lead end-to-end delivery of Databricks-based enterprise data platform programs. Drive cross-functional teams (data engineering, data science, DevOps, BI), define architecture standards, manage roadmaps, risks, KPIs, and stakeholder reporting. Oversee Delta Lake, medallion architecture, ETL/ELT and streaming pipelines, platform optimization, governance, security, and Agile/SAFe delivery.

Design, train, and productionize machine learning and time-series forecasting models on GCP. Ingest and process large datasets with SQL/Python, deploy pipelines using Vertex AI/BigQuery, translate model outputs for business stakeholders, and mentor junior data scientists while improving forecasting and operational decision-making.

Lead the strategy and roadmap for Canadian ETF analytics and data products, manage end-to-end product lifecycle from concept to post-trade, design pricing and distribution, engage institutional clients for discovery, and influence cross-functional stakeholders (Legal, Compliance, Technology, Sales) to deliver ETF analytics, liquidity metrics, basket composition data, and marketplace data solutions.

Lead and manage applications development teams for Big Data platform delivery. Hands-on development and troubleshooting across Hadoop ecosystem, build/oversee data pipelines (batch and streaming), ensure CI/CD and cloud integration, mentor engineers, manage stakeholders, budgets, and compliance with policies and controls.

Lead hands-on development and delivery of big data platform projects. Manage and mentor engineers, design data flows and cloud/microservice architectures, ensure DevOps/CI-CD practices, mitigate risks, and collaborate with business and product stakeholders to deliver high-quality data products.

Design, build, and maintain Spark-based data pipelines and platform services for sales compensation calculations. Improve data modeling, reliability, observability, testing, and scalability. Partner with Product, Engineering, and Analytics to translate business requirements into deterministic, production-grade data systems and participate in design and code reviews.

Build, maintain, and scale ETL pipelines and data infrastructure supporting analytics, data science, and data products. Collaborate with data scientists, analysts, engineers, and product managers to improve architecture reliability and performance. Develop Python tools and services, work with batch and streaming systems, research new technologies, and promote effective data engineering practices. This is a 12-month fixed-term maternity cover role with hybrid work in Toronto.

Owns product definition quality for complex data product opportunities, from problem framing through Ready-to-Build handoff. Authors product specifications, metric tables, and agent context documents; makes deployment channel decisions; maintains business context layers; and represents product definition in reviews with Platform Engineering and Data Science/ML. Establishes standards, coaches Solutions Analysts without formal authority, and ensures clean technical handoffs and well-defined model requirements.

Build and deploy end-to-end decision science systems (forecasting, experimentation, optimization, pricing, recommendations). Lead production-grade batch and real-time pipelines, partner with MLEs for deployment, drive domain impact, establish modeling standards, and mentor junior data scientists.

Develop and enhance AML/ATF transaction-monitoring solutions using Python, machine learning, network analysis, NLP, and advanced analytics. Analyze large datasets to identify elevated financial and reputational risk, tune detection models, visualize performance, document methodologies, and recommend improvements. Collaborate with AML analytics teams, business partners, industry peers, and vendors while researching emerging technologies and best practices.

Leads governance and portfolio enablement for Canadian Personal Banking AI and analytics initiatives. Oversees lifecycle progression, prioritization, risk and control coordination, governance readiness, and executive reporting. Partners with business, technology, model risk, compliance, cybersecurity, and data governance teams to operationalize AI standards. Develops Power BI dashboards, delivers training and communications, supports change management, and promotes responsible AI-assisted productivity improvements.

Leads TD Procurement’s data, reporting, analytics and insights function, including strategy, governance, executive reporting, data storytelling, predictive analytics, AI-enabled insights and self-service capabilities. Oversees interconnected procurement, risk, financial and operational data, defines analytical requirements, partners with technology teams, identifies trends and opportunities, and leads complex initiatives. Manages and develops a team of analytics professionals while advising senior leadership and advancing data-driven transformation.

Wayvia is building a talent pipeline for engineering roles focusing on backend systems, data pipelines, and product engineering. They seek curious and collaborative engineers to connect with and solve future challenges.

Serve as a trusted advisor for complex consulting engagements in Data Management. Lead solution scoping, architecture, feasibility assessment, design assurance, and implementation alignment with SimCorp standards. Own global consulting practice artifacts, improve methodologies, document best practices, enable consultants, and mentor aspiring architects. Collaborate with Sales, Product Management, Value Streams, project teams, and senior clients to resolve domain challenges and influence strategic architecture decisions.

Design, build, and maintain scalable data platforms, architectures, and pipelines using Azure Fabric, Databricks, and Snowflake. Optimize data workflows for reliability, scalability, performance, security, and compliance. Perform data modeling and ETL/ELT, improve data quality, and collaborate with analysts, scientists, engineers, and stakeholders to deliver tailored data solutions.

Design, build, and maintain scalable data platforms, architectures, and pipelines using Azure Fabric, Databricks, and Snowflake. Develop batch and real-time ingestion frameworks, optimize data workflows, perform ETL/ELT and data modeling, and maintain production-grade transformation layers. Ensure data security, compliance, reliability, and performance while collaborating with analysts, scientists, engineers, and nontechnical stakeholders to deliver effective data solutions.

Leads the planning, technical design, development, integration, and maintenance of complex enterprise applications for Finance Technology. Designs scalable data solutions and infrastructure, builds Databricks and Azure Data Factory pipelines, develops technology roadmaps, leads complex projects, conducts code reviews, and mentors developers. The role requires strong data engineering, cloud, programming, CI/CD, problem-solving, communication, and technical leadership skills.

Leads enterprise data engineering strategy and delivery, including secure data lifecycle management, cloud and hybrid data platforms, governance, integration, warehousing, streaming, and scalable pipelines. Builds ETL infrastructure and analytics tools, improves processes through automation, supports stakeholders, and advises senior leaders on complex data challenges. The role also applies AI, machine learning, MLOps, and modern data architecture practices.

Designs and operates ETL pipelines, data flow channels, processing systems, and modeling solutions supporting the full data lifecycle. Maintains data quality, governance, privacy, security, storage, and integration processes; monitors data systems and troubleshoots issues. Supports cloud data management, data-sharing policies, validation, system performance improvements, and enterprise data standards while applying risk-management principles in a banking environment.

Develop and scale investment-focused analytical data products, including financial datasets, ETL/ELT pipelines, dbt models, semantic layers, Power BI reports, and Streamlit applications. Partner with investment teams to translate quantitative requirements into reliable, governed, cloud-native solutions. Own products throughout their lifecycle, implementing data quality controls, orchestration, monitoring, documentation, and operational support. Contribute to AI-ready datasets and analytics modernization across portfolio management, performance, risk, trading, and research workflows.

As a Photonics Data Analysis Engineer, you will develop software for analyzing and measuring photonic devices, requiring advanced mathematical skills and strong programming expertise, particularly in Python and data visualization.

Develop, maintain, test, and optimize scalable data pipelines and Data Lakehouse architecture on AWS. Design storage and data models, manage Redshift/RDS/DynamoDB/S3, use Glue/EMR/Lambda for processing, enforce security and compliance, optimize performance and costs, and collaborate with analysts and data scientists to enable analytics and BI.

Design, build, and maintain scalable real-time and batch data pipelines and data lake architecture using AWS and big-data tools. Own end-to-end AI/ML workflows, perform feature engineering and statistical modeling for operational efficiency and financial forecasting, and provision infrastructure with Terraform/CloudFormation while optimizing pipeline performance.