Top Data Engineer Jobs in Toronto

The Data Engineer will develop and maintain data pipelines, ensure data quality, and collaborate with business teams to implement insightful data solutions.

Designs and maintains reliable data pipelines and computed tables using SparkSQL and PySpark. Integrates IoT, product, customer, external, unstructured, and ML-related data into scalable data models supporting analytics, causal inference, model training, and dashboards. The role requires ETL development, data orchestration, API integration, strong data quality and uptime, and collaboration with Data Science, AI/ML, Analytics, and engineering teams. This is a remote position for candidates residing in Canada.

Design, build, automate, optimize, and support complex cloud data pipelines and ETL/ELT processes. Develop unified Databricks data platforms, data lakes, lakehouses, warehouses, and dimensional models. Integrate and migrate data across Oracle, Azure, Databricks, and Microsoft Fabric. Create Azure Data Factory pipelines, implement CI/CD and DataOps practices, and perform performance tuning, monitoring, troubleshooting, and data provisioning for large-scale enterprise environments.

Performs data engineering across modeling, profiling, quality, ingestion, ETL, metadata, lineage, and governance. Develops complex data models, ETL frameworks, and enterprise data solutions; supports testing, deployment, release management, architecture reviews, and issue resolution. Partners with technical and business stakeholders, ensures compliance with data and security standards, leads engineering initiatives, mentors team members, and communicates complex technical details to nontechnical audiences.

Develop and maintain ETL processes, SQL Server databases, SSAS data models, SSRS reports, and Power BI dashboards. Build unified data models across ERP systems, improve BI performance, establish development standards, and translate cross-functional business requirements into scalable data and integration solutions. The role also supports modernization of Sofina’s data environment and strategic reporting for finance, sales, supply chain, and other stakeholders.

Build a high-performance frontend data platform for exploring and analyzing hardware telemetry. Develop React and TypeScript applications, interactive time-series visualizations, data visualization plugins, complex application state management, and LLM-assisted analysis workflows. Collaborate directly with hardware engineers and customers to define interfaces, prototype workflows, demonstrate features, and incorporate feedback into product development.

Design, build, deploy, and maintain scalable multi-source ELT pipelines and enterprise data warehouse infrastructure. The role involves optimizing data systems for cost, performance, reliability, and SLAs; curating campaign and business data; creating monitoring and automation; and collaborating with engineering, analytics, SRE, and platform teams to deliver reliable data products.

Leads the design, development, and optimization of scalable data architectures, ETL pipelines, ingestion frameworks, data quality processes, governance practices, and audit systems. Partners with cross-functional teams to translate requirements into data solutions, analyzes data for business insights, communicates recommendations, and mentors Data Engineers. The role also serves as a technical authority and supports continuous improvement across data engineering practices.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, improve reliability and quality, and mentor engineers. Build and monitor production LLM and agent systems, automate workflows with AI, and deliver high-quality Python and SQL while validating AI-generated code. Partner with Marketing, Sales, BI, Data Science, and R&D teams.

Lead design, build, and deploy of large-scale data platforms for LLMs, RAG, and agentic AI systems. Hands-on coding, architecting fault-tolerant pipelines, establishing MLOps/DataOps best practices, mentoring engineers, and operationalizing research into production across Exabyte-scale distributed systems.

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.

Design, build, and maintain Azure-based data pipelines, data models, KPI marts, and reporting layers for financial and operational analytics. Translate business requirements into scalable solutions, perform reconciliation and root-cause analysis, implement data quality controls, execute QA and UAT, and document data flows and metric definitions. Collaborate with Finance, Business, Analytics, Technology, and infrastructure teams to deliver accurate, governed, production-ready datasets and improve automation and reliability.

Designs and maintains Azure-based ETL/ELT pipelines, data models, KPI marts, and reporting datasets for financial and operational analytics. Investigates data discrepancies, performs reconciliation and root-cause analysis, implements data quality controls, and supports QA, UAT, and production releases. Partners with Finance, Business, Analytics, and Technology teams to translate requirements into scalable solutions, document metrics and data flows, and automate processes.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

Leads enterprise data quality and governance initiatives, including data profiling, validation, cleansing, monitoring, metadata management, cataloging, lineage, classification, and data protection. Partners with business stakeholders, data owners, engineering, architecture, analytics, and security teams to define quality standards, remediate issues, improve governance-platform adoption, and establish data sensitivity controls. Provides technical leadership across global data environments and supports AI-enabled classification and responsible data use.

Design, build, and maintain scalable data pipelines, warehouses, and data models using GCP technologies. Develop ETL/ELT processes, dynamic Airflow workflows, and modular Python-based solutions for large-scale data processing. Optimize pipeline performance, query efficiency, and cloud costs while integrating new data sources. Collaborate with data scientists, analysts, and stakeholders to deliver reliable data products supporting business intelligence, machine learning, and strategic decision-making.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Design, build, operate, and improve production data pipelines using Airflow, BigQuery, and GCP. Develop scalable data models, backend APIs, dashboards, and AI-powered workflows that transform documents into structured customer-facing intelligence. Implement LLM and agentic systems with retrieval, tool calling, evaluation, quality controls, and production safeguards. Collaborate with analysts, product teams, and stakeholders while independently solving ambiguous engineering problems.

Build and scale backend-focused software for Braze’s petabyte-scale Data Lakehouse. Develop data pipelines, asynchronous processing, ETL applications, and end-to-end features across platforms such as Snowflake, ClickHouse, and Databricks. Drive architectural decisions, contribute to technical strategy, and deliver scalable web applications handling high volumes of data and traffic in an agile environment.

Build and maintain backend-focused, end-to-end software for Braze’s petabyte-scale Data Lakehouse platform. Develop infrastructure and tools supporting Snowflake, ClickHouse, and Databricks, manage high-volume event data, and deliver front-end experiences when needed. Collaborate across teams, apply strong software design and algorithms knowledge, and help scale systems handling billions of daily rows and significant customer and internal workloads.

Develops and supports data and AI products, translating business requirements into technical solutions. Responsibilities include data pipeline and warehouse development, testing, documentation, defect resolution, technical design, automation, reusable framework creation, process improvement, and stakeholder collaboration. The role requires ETL/ELT, SQL, NoSQL, Python, Spark, Scala, cloud, Hadoop, data profiling, batch and streaming data, and emerging AI capabilities.

Build and scale reliable Snowflake data pipelines, ML pipelines, monitoring systems, and AI-driven agentic tooling that automates pipeline generation, testing, and maintenance. The role partners with analysts, data scientists, product teams, and analytics engineers to deliver scalable end-to-end data infrastructure for Babylist’s e-commerce platform.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.