Top Data Jobs in Toronto

The Manager of Data Operations will lead a team to ensure the reliability and scalability of data platforms, enforce operational standards, and implement CI/CD practices. Responsibilities include monitoring data quality, incident management, and collaborating with various teams to enhance data operations.

The Principal Data Engineer at RAVL will design and implement enterprise-grade data and AI platforms, mentor engineers, and influence architectural standards across teams.

The Data Engineer will design and manage data pipelines and architectures, ensuring data quality and security while collaborating with cross-functional teams for effective analytics and reporting.

The role requires developing solutions using IBM DataPower, with a focus on creating WSP, XFW, MPGW, and managing certificates and encryption. Candidates should have strong skills in troubleshooting and working with various DataPower devices.

Develop and implement AI risk assessment and pricing frameworks by evaluating AI models, creating rating tables and pricing models, collaborating with underwriters, building Python-based analytical tools, and researching emerging AI risks and mitigation strategies.

The Data Engineer will analyze and interpret data to drive insights, create dashboards, and enhance product performance, collaborating with cross-functional teams.

As a Data Analyst, you will analyze user behavior and product performance, creating reports and dashboards to aid decision-making and optimize user experience.

The Senior Data Scientist will drive the development and implementation of advanced statistical methodologies, lead client consultations, and manage complex analyses, while providing innovative analytical solutions and mentoring team members.

Looking for a Data Engineer experienced in Dataiku to design and maintain data pipelines and infrastructure, ensuring data flow and integration across teams.

The Data Engineer will architect and implement advanced analytics capabilities, manage data pipelines, and optimize data processing using AWS cloud and various analytical tools.

Lead the technical delivery and design of scalable kdb+ solutions while mentoring junior developers and collaborating with financial analysts.

Collaborate with business users, develop and maintain code for applications, analyze time-series data, and mentor junior developers.

The Associate Director of Data Sciences drives SOTI's AI and data strategy, develops AI capabilities, and mentors teams to enhance product efficiencies.

Leads data architecture, integrations, product analytics, and client-specific analyses for an AI-enabled life sciences platform. Partners with engineering, product, solutions, and client services teams to build scalable, AI-ready data systems and improve measurement insights. Manages and mentors data analysts and engineers while supporting pharmaceutical and life sciences customer needs.

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.

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 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.

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.

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.

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.

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.

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.

Leads MAU analytics and data science, managing decision scientists and data scientists while setting quality standards. Owns MAU driver modeling, forecasting, causal analysis, experimentation, and exploratory work supporting growth strategy. Establishes consistent analytical practices, partners with revenue, product, engineering, and data engineering leaders, and embeds the team in roadmap planning. Promotes AI-native workflows through coding agents, automation, and analytical tools while coaching team members into strategic advisors.