Top Data Engineer Jobs in Toronto

The Lead Engineer for BI will design data solutions, lead project teams, mentor, and drive BI strategy aligning with business needs.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

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.

Own and drive the architecture and roadmap of ClickUp's data platform. Build scalable, reliable data pipelines and AI/ML infrastructure using AWS serverless, Snowflake, dbt, and Terraform. Lead cross-team technical initiatives, optimize cloud costs, establish engineering standards (observability, testing, CI/CD), mentor senior engineers, and influence org-wide architecture decisions.

Lead design, build, and operate scalable Generative AI systems (LLM apps, RAG pipelines, multi-agent systems) in production using Databricks and cloud infrastructure. Own CI/CD, monitoring, evaluation frameworks, optimize retrieval/generation, mentor engineers, and influence product roadmap.

Lead development and maintenance of the core data platform: build performant pipelines, data warehousing, and ML infrastructure; partner with data scientists to produce production datasets; ensure data governance, quality, lineage, and accessibility; integrate cross-platform data sources; automate reporting and deliver stakeholder insights to enable data-driven decisions.

Own and maintain data platform infrastructure: provision Snowflake via Terraform, build CI/CD for dbt, implement data quality and observability, extend the OpenMetadata catalog, create developer tooling and reproducible environments, and ensure secure, production-ready data pipelines.

Design, build, and optimize lakehouse data pipelines and curated data products using Python and SQL. Improve performance, observability, governance, and cost-efficiency. Own data modeling, orchestration, data quality, and secure access patterns. Partner with product, engineering, and analytics teams, troubleshoot production issues, and mentor junior engineers to advance the organization’s data platform and AI readiness.

Build and operate governed, reproducible AWS-based data pipelines and analytical products. Stand up managed AWS foundation, deliver top-priority data products, support product teams to follow architecture, and apply disciplined reliability and governance while using low-code only when appropriate.

Lead a team to design, build, and operate large-scale data pipelines and warehouses, ensure data quality and SLAs, collaborate cross-functionally, leverage AI/LLM, and drive key data initiatives from planning to delivery.

Design and own end-to-end data quality systems for managed AI data studies: define rubrics, sampling plans, automated checks, launch gates, drift detection, dashboards, and calibration. Investigate integrity issues, run root-cause analysis, build automation in Python/SQL, and partner with Product, Engineering, and Operations to embed quality controls, train reviewers, and scale repeatable quality playbooks across programs.

Plan and execute a zero-downtime migration of stateful data systems (Druid, Spark, Redpanda, Airflow, PostgreSQL, Elasticsearch) from bare-metal VMs to Kubernetes on OpenStack. Codify infrastructure as code, implement safe StatefulSet/PVC and upgrade patterns, and own day-to-day operational health. Build observability, SLOs, runbooks, KPIs, and optimizations. Partner with SRE on hardware, networking, and Kubernetes fundamentals while owning data applications end-to-end.

Lead design and implementation of scalable, secure Forma Data cloud services. Act as technical lead across full software lifecycle, write and operate cloud services, mentor engineers, solve complex distributed-system problems, and collaborate globally to integrate SaaS products with Autodesk tools.

Responsible for designing and operating data architecture, building ETL/ELT pipelines, implementing data quality frameworks, and collaborating on ML initiatives.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Perform L3 network troubleshooting and RCA on production enterprise networks, review telemetry/logs/configs, label and annotate network behaviors for ML training, and help define schemas and data pipelines to support AI-driven infrastructure.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

Design and implement automated data quality frameworks and tests for BigQuery-based pipelines. Collaborate with data engineers and product to define data contracts and quality gates, build observability and anomaly detection, validate complex ELT/ETL processes, and integrate data tests into CI/CD workflows. Drive improvements in documentation, governance, and pipeline reliability.

As a Software Engineer, you will create tools and pipelines for AI development, manage data solutions for ML teams, and ensure high-quality code.

As a Staff Backend Engineer, you will develop complex backend solutions, focusing on the Spark-based data platform while ensuring code quality and collaborating closely with teams.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Design, build, and optimize cloud-native lakehouse data pipelines and curated data products using Python and SQL. Own data modeling, performance tuning, observability, governance, and reusable platform patterns to support analytics, embedded reporting, operational workflows, and AI/ML datasets. Mentor engineers, lead reviews, and improve platform reliability, cost efficiency, and data quality.

As a Software Engineer in Data Infrastructure, you will build and maintain high-performance data storage systems for AI training, collaborate with experts, and optimize data processing.