Top Data Engineer Jobs in Toronto

Design, build, and maintain ETL/ELT pipelines and data models to ingest and process customer/contact data. Implement SQL-based transformations, data quality rules, validation, monitoring, and reconciliation. Optimize performance and storage, perform root-cause analysis and remediation, support QA/UAT, and drive automation and AI-assisted workflows to maintain trusted golden records.

Design, build, optimize, and monitor scalable data products, pipelines, APIs and analytics solutions on Azure. Mentor junior engineers, implement CI/CD and infrastructure-as-code, ensure security and performance, and partner with stakeholders to deliver production-ready data for analytics and automation.

Build, own, and evolve Flinks' data and ML platform: BigQuery/dbt pipelines, Airflow ingestion, Kubeflow/Vertex training, model serving, governance, observability, and cross-team data contracts to support production ML and analytics at scale.

Lead design, development, testing, implementation and support of data and AI products. Translate business requirements, provide technical direction, mentor developers, build automation and frameworks, improve delivery processes, and perform data analysis, profiling and regression testing.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

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.

Design and operate distributed inference systems for LLMs, build large-scale data pipelines, and debug production issues while collaborating with researchers and ML engineers.

As a Staff Software Engineer, you'll design and deliver a core data platform, focusing on architecture, pipeline components, and AI integration. This hands-on role emphasizes creating production code and establishing technical standards within a small team.

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.

Design, build, and maintain scalable cloud data pipelines and architectures. Develop and optimize ETL/ELT workflows, manage data warehouses (primarily BigQuery), implement governance and security, collaborate with engineers and analytics teams, enable ML/AI use cases, and maintain documentation, lineage, and high-availability pipelines.

Build and operate backend-heavy full-stack applications, APIs, dashboards, and data pipelines. Integrate ML/AI models into production workflows, design data models, containerize and deploy services with Docker/Kubernetes, improve reliability and observability, and collaborate with research, data science, product, and design teams.

Build and maintain core financial backend systems (trading, ledger, billing), transaction processing, reconciliation, and portfolio state management. Design and scale real-time and batch data pipelines, architect APIs and backend services, ensure reliability and observability, and contribute to distributed system design (event-driven, microservices, streaming). Partner with ML teams as needed.

Design, build, and operate cloud-native, serverless ClickHouse database infrastructure. Develop and maintain Kubernetes operators, improve metrics pipelines, support data ingestion/transformation (Spark/Kafka), collaborate with core DB and data plane teams, and ensure scalable, highly available distributed systems with production on-call responsibilities.

Design, build, and deploy scalable, secure data solutions on-premise and in Azure. Collaborate with engineering, security, and ops to implement modern data practices (Databricks, Snowflake, Python, SQL), produce solution designs, support project delivery, and promote DevSecOps and cloud best practices.

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.

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.

Build and maintain responsive Angular UI components, collaborate with UX and backend teams, implement REST APIs and Python backend scripts, integrate basic ML models into data workflows, and participate in the full SDLC within Agile teams while ensuring code quality and performance.

Design, develop, test, and deploy high-performance Apache Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and platform performance, build data pipelines using HDFS, Hive, Impala, HBase, and Kafka, ensure data integrity and security, collaborate with data scientists and analysts, and implement version control and CI/CD for deployments.

Senior Java Big Data Developer with 8+ years building applications on Cloudera/Hadoop ecosystems using Spark and MapReduce. Responsible for designing performant Java (1.8+) solutions, data analysis of large datasets, unit testing (JUnit), and collaborating with/managing global technology teams. Preferred skills include Unix/Python scripting and familiarity with Hive, Impala, YARN, and Kafka.

Design and maintain data pipeline architecture, manage AWS services, optimize data systems, and ensure efficient ETL operations across various data sources.

Design, build, and maintain scalable data systems and ETLs on GCP; connect production data to business systems; generate analytics and ML-driven insights for GTM teams; work cross-functionally and introduce modern tools (Airflow, DBT, Presto, Hightouch).

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.