Top Data Engineer Jobs in Toronto

Design and build scalable backend systems, APIs, data models, event-driven services, experimentation platforms, and personalization systems. Partner with Product, Data Science, Analytics, and Design teams to support customer acquisition and engagement. Establish secure, observable, high-performance engineering practices, mentor junior engineers, and participate in monitoring, on-call, and operational support.

Design, build, and maintain scalable data pipelines and infrastructure using Spark, Kafka, cloud platforms and containerization. Ensure data integrity, ETL, high availability, performance tuning, observability, and collaborate with AI/data science teams to enable analytics and ML solutions.

Design electrical systems for hyperscale, colocation, and mission-critical data centers across North America. Responsibilities include electrical calculations, equipment sizing, drawings, one-line diagrams, specifications, code compliance, multidisciplinary coordination, design reviews, and construction-phase support. The role requires knowledge of Canadian electrical and building codes, resilient power infrastructure, and collaboration with clients, contractors, vendors, and utilities.

Own the technical direction, development, reliability, and migration of Faire’s data infrastructure. Build CDC and streaming ingestion pipelines from production databases through Kafka into Iceberg on S3, operate Airflow and Fivetran, and implement data contracts, quality checks, SLAs, and observability. Lead cross-team initiatives, mentor engineers, manage incidents and on-call responsibilities, and partner with data scientists, analysts, and engineering teams to deliver dependable analytical data platforms.

Lead the architecture and hands-on delivery of end-to-end Azure data platforms, including ingestion, transformation, lakehouse modeling, analytics, APIs, and activation. Build batch and streaming pipelines with Microsoft Fabric, Azure Data Factory, Databricks, Python, PySpark, and SQL. Implement governance, security, CI/CD, MCP and agentic AI integrations, while troubleshooting production systems. Mentor engineers, advise client stakeholders, contribute to presales, and establish reusable engineering standards.

Design, build, and maintain Databricks/Spark ETL pipelines and Bronze-Silver-Gold architectures; implement event-driven streaming with Pub/Sub and Protocol Buffers; prepare datasets for LLM fine-tuning and embeddings; integrate third-party sources (Shopify, Klaviyo); implement attribution, CLV, and analytics models; ensure data quality, monitoring, and idempotent retry-safe pipelines.

Design, build, deploy, and optimize high-performance distributed data platform services supporting analytics and machine learning. Responsibilities include developing scalable streaming infrastructure, participating in design and code reviews, tuning performance, debugging production issues, collaborating cross-functionally, and mentoring engineers. The role requires expertise in distributed computing, messaging, data processing, cloud technologies, software engineering principles, and reliability practices.

Design, build, and operate scalable data platforms covering ingestion, orchestration, modeling, quality, observability, governance, and BI enablement. Develop batch and streaming pipelines with Airflow, Snowflake, dbt, Terraform, and CI/CD tooling. Establish engineering standards, data access controls, and AI-assisted workflows while enabling software, analytics, and non-technical users to build production-grade data products. Provide technical leadership, vendor management, documentation, training, and full DevOps ownership.

Develop and maintain scalable Java and big data applications across the software development lifecycle. Build distributed data-processing tools, REST APIs, and automation; analyze and debug systems; conduct test-driven validation; review code; and collaborate with internal teams and vendors. The role works with technologies including Apache Hudi, Trino, Spark, Kafka, cloud storage, and distributed computing platforms while promoting performance, reliability, modern engineering practices, and continuous technical improvement.

Owns biological knowledge representation through LinkML and Biolink ontology engineering, semantic mappings, metadata harmonization, and schema governance. The role administers agentic curation and MCP tooling, develops LLM-driven extraction and validation workflows, and establishes automated quality controls, provenance, CI/CD, and evaluation standards. This senior individual contributor translates biological research requirements into canonical data models supporting genomics, single-cell, screening, imaging, and prior-knowledge datasets.

Own and scale a multi-cloud data platform across AWS and Azure. Manage Apache Airflow, Kubernetes, Terraform, Helm, ArgoCD, CI/CD, observability, big data infrastructure, governance tooling, and FinOps initiatives. Provide end-to-end architectural and operational ownership, improve developer tooling, define platform roadmaps, manage incidents, enforce engineering standards, and mentor data platform engineers.

Lead architecture and hands-on engineering of Pantheons data platform: extend ingest, curation, and retention services; build ML/LLM-ready data infrastructure; mentor engineers; operate in a DevOps model; ensure data governance, observability, reliability, and cross-functional partnership.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, workflow automation, evaluation, and observability capabilities. Work hands-on with Python, SQL, dbt, lakehouse data, cloud infrastructure, model selection, and inference-cost optimization. Establish reusable engineering patterns and mentor teams through high-quality code and system design.

Build and maintain Betstamp’s real-time market data platform, including ingestion from 100+ sources, Redis Streams and ClickHouse pipelines, and APIs delivering sub-second odds data. Own feed latency benchmarking, integrations, normalization, reliability monitoring, and line-history infrastructure. Use AI coding tools responsibly while investigating performance and production issues. Work with senior engineers on architecture and high-impact changes. The role is based in Toronto or requires relocation, with an anticipated September 2026 start.

Build and maintain real-time sports betting and prediction-market data systems. Responsibilities include improving scraper ingestion across 100+ integrations, managing Redis Streams and ClickHouse pipelines, optimizing PostgreSQL and ClickHouse queries, maintaining schemas and migrations, and creating Prometheus/Grafana monitoring. The role also emphasizes AI-assisted development, debugging, testing, and investigation, with increasing ownership across the backend stack alongside senior engineers.

Build production AI harness systems for Kinaxis’s Databricks-based enterprise Data Fabric. Responsibilities include developing agent execution frameworks, integrating LLMs with governed data, implementing tool calling and workflow controls, enforcing multi-tenant isolation, and delivering APIs, SDKs, libraries, templates, and operational runbooks. The role requires distributed-systems engineering, cloud data-platform experience, secure multi-tenant design, and practical expertise in agent orchestration, retrieval, evaluation, guardrails, metadata, lineage, and governance.

Lead the architecture and hands-on engineering of Cloudbeds’ Snowflake and dbt data platform. Own data modeling, warehouse design, ingestion, orchestration, DataOps, CI/CD, governance, observability, performance, cost optimization, and AI/ML readiness. Mentor analytical engineers, evaluate emerging tools, establish data quality and security standards, and collaborate with engineering, infrastructure, data science, and business stakeholders in a fully remote environment.

Provide hands-on technical leadership for Benevity’s data platform. Design and build scalable streaming and batch pipelines, AWS lakehouse and medallion architecture, metadata governance, semantic layers, and data products. Establish configuration-driven workflows, data contracts, quality standards, observability, and automated CI governance. Partner with architects and product stakeholders, develop AI-assisted engineering tools, document architecture, deliver proofs of concept, and mentor engineers while guiding platform modernization.

Own and evolve HOOPP’s investment data warehouse, pipelines, enterprise data model, and production support processes. Investigate data and platform issues, perform root-cause analysis, maintain data integrity and governance, and deliver sustainable solutions across investment, risk, finance, and technology teams. Develop AI-assisted projects using Python and related tools, improve monitoring and operational workflows, and manage access reviews, releases, documentation, and preventive actions.

Build and operate scalable AWS data foundations supporting analytics, search, and AI use cases. Develop structured and unstructured data pipelines using Snowflake, S3, lakehouse architectures, and distributed processing. Establish engineering standards, automated testing, infrastructure-as-code, CI/CD, data quality, lineage, and observability. Collaborate with data scientists, AI engineers, and business partners to productionize ideas, mentor engineers, and deliver secure, reliable, measurable solutions.

Designs and delivers scalable Global Transaction Banking data and platform solutions. Provides technical leadership for architectures, engineering standards, roadmaps, resilience, reliability, and platform adoption. Builds distributed, event-driven, big-data systems and APIs while managing technical debt, DevOps practices, and cloud environments. Partners with engineering, product, and business stakeholders, communicates complex concepts, estimates delivery efforts, and promotes Agile, customer-centric engineering culture.

Build and operate scalable data pipelines supporting batch and streaming workloads, AI and ML systems, analytics, and sports intelligence products. Responsibilities include ETL/ELT development, data modeling, orchestration, retrieval pipelines, data quality, lineage, monitoring, infrastructure optimization, and collaboration with ML, product, and analytics teams.

Designs, develops, tests, and maintains data integrations connecting customer cloud and on-premises systems to the Prophix platform. Builds reusable APIs and real-time interfaces, contributes to integration architecture and strategy, automates reliable data processes, and supports stakeholders and engineers. The role also involves project management, technical leadership, mentoring, enforcing integration standards, and helping customers transition to secure, scalable cloud-based data environments.

Build and maintain scalable data pipeline architecture, process complex datasets, improve data delivery and infrastructure, and support data initiatives across engineering, product, business intelligence, and data science teams. Develop extraction, transformation, and loading infrastructure using Spark, EMR, Kafka, and other big data technologies while optimizing production-scale data and machine learning pipelines.

Design and maintain scalable backend, full-stack, and AWS data infrastructure supporting millions of daily events and hundreds of integrations. Set technical direction for data pipelines, lead architecture and planning, troubleshoot production issues, collaborate across teams, manage work in Jira, research improvements, and mentor engineers. The role owns initiatives end to end while developing reliable, cost-efficient serverless systems using Node.js, TypeScript, React, and AWS services.