Top Data Engineer Jobs in Toronto

Design, build, and maintain scalable data infrastructure and ETL pipelines using Python, SQL, PySpark, and Microsoft Fabric. Collaborate with Analytics and Data Science to translate requirements, optimize queries, investigate and resolve data issues, and support reliable pipeline operations and system architecture.

Design, build, and maintain reliable, scalable ETL/ELT pipelines, support data migrations, prepare analytics and AI-ready datasets, implement data quality and observability, support Power BI models, and collaborate with analytics, IT, governance, and AI teams to deliver production data solutions.

Lead design and implementation of a federated Lakehouse data platform, unify data silos, define governance/observability, enable secure AI/ML (RAG/vector readiness), mentor architects/engineers, and present roadmaps to executives.

Looking for a Data Engineer experienced in Dataiku to design and maintain data pipelines and infrastructure, ensuring data flow and integration across teams.

The Data Engineer will architect and implement advanced analytics capabilities, manage data pipelines, and optimize data processing using AWS cloud and various analytical tools.

The Principal Data Engineer at RAVL will design and implement enterprise-grade data and AI platforms, mentor engineers, and influence architectural standards across teams.

The Data Engineer will design and manage data pipelines and architectures, ensuring data quality and security while collaborating with cross-functional teams for effective analytics and reporting.

The Data Engineer will analyze and interpret data to drive insights, create dashboards, and enhance product performance, collaborating with cross-functional teams.

The Data Engineer is responsible for designing and developing cloud-native data pipelines, integrating machine learning models, and ensuring the reliability and scalability of data solutions in a collaborative environment.

The Software Engineer for Data Infrastructure & Acquisition will manage data collection for model training, optimize ingestion pipelines on GCP, and collaborate with the AI team to enhance data quality and scalability.

Owner of Float's Snowflake architecture and ingestion layer: build orchestration, observability, data quality and contracts, CI/CD for data code, support ML pipelines, and collaborate with analytics, data science and product teams to deliver reliable, cost-efficient, governed data.

The Big Data and SQL Developer is responsible for designing and implementing scalable Big Data solutions, mentoring engineers, and engaging with stakeholders, balancing technical tasks with leadership duties.

The Data Engineer will assist clients with SAP security design, implement SAP security, develop authorization profiles, and conduct security reviews.

Design and build an AWS-based lakehouse and scalable enterprise data platform. Create governed data products and automated Python/SQL pipelines. Establish standards for data quality, governance, security, observability, and CI/CD. Partner with engineering, product, finance, and business teams to enable analytics and AI and define the platform's long-term vision.

The Senior Data Engineer will scale the data ecosystem, construct data models, build systems for KPI tracking, and ensure data safety.

Develop, maintain, test, and optimize scalable data pipelines and Data Lakehouse architecture on AWS. Design storage and data models, manage Redshift/RDS/DynamoDB/S3, use Glue/EMR/Lambda for processing, enforce security and compliance, optimize performance and costs, and collaborate with analysts and data scientists to enable analytics and BI.

Design, build, and optimize Databricks-based data and ML platforms. Own ETL pipelines, Delta Lake lakehouse, MLflow model lifecycle, MLOps, cluster optimization, RAG/LLM solutions, BI integration, and data governance with Unity Catalog.

Senior Data Engineer responsible for designing and validating data solutions, consolidating disparate data sources, documenting functional specifications, reviewing data models, supporting production deployments, investigating data quality issues, and collaborating with stakeholders and technology leads to ensure data completeness and lineage.

Design, develop, and maintain data engineering solutions across the SDLC using Python (primary) and Scala (secondary). Build distributed data pipelines on Hadoop/Spark ecosystems, extend data processing platforms (Spark SQL, Hive, Starburst), manage CI/CD, and deploy on Kubernetes/OpenShift. Collaborate with stakeholders in Agile teams and ensure maintainable, reusable code.

Lead technical design, enhancement, and support of Ab Initio ETL applications; architect and migrate ETL/ELT pipelines to AWS (S3, Glue, Redshift, Lambda); drive performance, governance, and CI/CD; operate in Agile/Scrum or SAFe using Jira; mentor engineers and coordinate cross-functional delivery while ensuring stability of existing platforms.

Design, build, and deliver scalable Salesforce solutions across Sales Cloud, Data Cloud/Data360 and Agentforce. Lead data integration, identity resolution, data modelling, AI-assisted workflows, Flow/Apex development, LWC/Visualforce components, and platform administration. Own end-to-end delivery, governance, data quality, and release management while partnering with sales and data teams to implement automation and AI-enabled sales workflows.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

Lead architecture and delivery of an AI-ready data platform: define enterprise data models, medallion/lakehouse patterns, data governance, observability, secure interoperability, and mentor teams to build production-ready AI data products.

Lead design and delivery of an AI-ready, AWS-native enterprise data platform and lakehouse. Define architecture patterns, modeling standards, ingestion/CDC, observability, governance, and secure interoperability. Mentor teams, contribute hands-on to platform modernization, enable vector/RAG AI workflows, and provide reference implementations to drive consistent, scalable data products and analytics.