Top Data Engineer Jobs in Toronto

Architect and build core distributed systems for high-throughput real-time and batch data ingestion, processing, and storage. Ensure fault-tolerance, scalability, and strong query performance for time-series and spatial data. Develop automated tooling for data quality and lineage, collaborate on reusable abstractions, and write production-ready code (Java/Python).

Develop and maintain reliable, secure, high-throughput cloud data services. Collaborate with product and architecture teams to design cloud-native solutions, build and evolve APIs, improve processes, document and present solutions, mentor peers, and work with CI/CD pipelines and cloud databases to deliver scalable services used by millions.

Design and build scalable ETL data pipelines and RESTful APIs/microservices. Improve performance, monitor and troubleshoot data flows, ensure data security/compliance, write tests, and collaborate cross-functionally.

Design, build, and maintain a robust data ecosystem and ETL pipelines to support digital campaigns, reporting, and analytics. Optimize data assets for performance and quality, partner with stakeholders to translate requirements into solutions, act as a data steward and subject-matter expert, and deliver iterative improvements within a Kanban continuous-delivery framework.

Build AI-native automation systems, developer tooling, and high-scale data ingestion for sportsbook odds and market feeds. Create AI workflows/agents that generate and validate code, maintain data feeds, automate debugging/testing/deployments, and monitor failures with automated remediation. Focus on internal automation, LLM orchestration, and distributed backend pipelines to accelerate the engineering team.

Lead design and implementation of a scalable data platform handling petabytes of telematics and time-series data. Build and optimize streaming and batch pipelines, design storage solutions, implement observability and data lineage, manage HA infrastructure, and write maintainable Java/Python code while shaping long-term architectural strategy.

The role involves building and optimizing big data pipelines, designing data architectures, collaborating with data scientists, and driving technical strategy for data processing infrastructure.

Design, build, and scale Snowflake ingestion pipelines and ML/LLM systems. Create agentic tooling to generate, test, and maintain pipelines. Collaborate with analysts, data scientists, and product teams to deliver reliable, high-performance data infrastructure and operationalize models for an AI-first e-commerce platform.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

Develop and extend data ingestion pipelines for AI model training, manage cloud infrastructure, and collaborate with AI scientists. Bring new audio data sources into the system.

Lead end-to-end data and ML pipelines for onboarding and fraud decisions: ingest streaming and batch sources, build feature platform and entity resolution, productionize ML models, enforce PII/data governance, and set technical direction while mentoring engineers and ensuring low-latency, reliable serving.

Design, build, and maintain scalable data platforms and pipelines using Azure Fabric, Databricks, and Snowflake. Implement ETL/ELT, optimize workflows for performance and reliability, ensure data security and compliance, perform data modelling and transformations, and collaborate with analysts, scientists, and stakeholders to improve data quality and analytics.

Design and optimize scalable data pipelines, develop reusable frameworks, troubleshoot production issues, and enforce data engineering standards while collaborating with stakeholders.

As a Senior Data Engineer at Nimble, you will manage the end-to-end data platform, build pipelines, enhance data modeling, collaborate with teams, ensure data security, and mentor others.

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.

The Lead Data Engineer will design and enhance data pipelines and infrastructure for data-driven decisions, mentor engineers, and establish data governance.

As a Senior Data Engineer, you will build a data platform, create data pipelines for market and trade data, and collaborate with traders and quants to enhance analytical workflows.

Lead design and delivery of scalable, governed data ingestion and integration pipelines for structured, semi-structured, and multimodal data to enable AI, ML, and Generative AI. Partner with product, data science, architecture, risk, and operations to define requirements, ensure data quality, lineage, security, and observability, and evaluate new technologies. Mentor engineers, drive cross-functional initiatives, improve platform resilience, and measure ingestion and AI data effectiveness.

The Guidewire Data Engineer will design schemas in Snowflake, develop ETL processes, optimize SQL queries, and integrate Guidewire with data platforms.

Design, build, and maintain the data warehouse and data lake, schemas, and reliable ETL/ELT pipelines to support AI agents and analytics. Implement data governance, quality checks, lineage, and access controls to meet ISO27001 and SOC2 Type 2. Partner with AI, InfoSec, and product teams to enable agent orchestration, integrate with Microsoft 365/Graph/OAuth, monitor platform health and costs, and support AI program planning and documentation.

Design, build and configure scalable Big Data applications and ingestion pipelines. Translate requirements into design and code, work with architects and business stakeholders, ensure solutions meet security and data privacy policies, and support CI/Agile SDLC practices while analyzing data stores and Hadoop cluster performance.

Lead end-to-end analytics and AI/ML initiatives: diagnose performance across customer/product/advisor dimensions, build scalable datasets, dashboards, segmentation, and predictive models, implement cloud-based ML solutions, produce executive-ready visualizations and stories, mentor teams, and drive adoption of advanced analytics across business stakeholders.

Lead design and delivery of enterprise-scale Azure data platforms and AI-powered automation. Build reusable frameworks, solve complex technical problems, mentor engineers, influence stakeholders, and champion data engineering best practices.

The Senior Software Developer will design and build scalable APIs and distributed systems, improve operational efficiency, and mentor other engineers, focusing on automation and data infrastructure.

Build and maintain Geotab's internal ML and generative AI platform, including model management, logging/monitoring, and alerting. Enrich large-scale telematics data, integrate data pipelines with data scientists, and support internal teams with infrastructure, documentation, and production GenAI applications and agents.