Top Data Engineer Jobs in Toronto

Design, develop, and optimize enterprise data pipelines and ETL/ELT integrations using Informatica IDMC. Build transformation workflows, integrate multiple data sources into cloud and enterprise environments, troubleshoot performance and data quality issues, and implement testing and monitoring. Collaborate with architects, analysts, engineers, and business stakeholders to deliver scalable data solutions, primarily for financial services clients.

Develop secure integrations enabling AI agents and applications to access exchange market data. Build backend services and APIs using Python, Java, or comparable technologies; implement authentication, authorization, identity management, licensing, entitlements, and usage controls. Apply market-data distribution expertise and AI integration patterns such as LLMs, agentic AI, and MCP while collaborating with financial institutions and technology stakeholders.

Leads hands-on data and software engineering across the full development lifecycle. Owns technical design, architecture, development, engineering standards, developer experience, and fault-tolerant application systems. Provides technical guidance to stakeholders, establishes best practices, contributes to high-level design, and mentors junior and intermediate engineers. The role requires expertise in application development, data modeling, cloud platforms, and modern software engineering practices within an agile financial-services environment.

Designs and develops complex data models, ETL jobs, ingestion processes, metadata, lineage, and data governance solutions. Performs data analysis, profiling, quality investigations, testing support, deployment coordination, and issue resolution. Partners with technical and business stakeholders, contributes to architecture and security practices, supports release management, and mentors team members while leading data engineering initiatives.

Performs advanced data engineering across data modeling, profiling, quality, acquisition, ingestion, ETL, metadata, lineage, and governance. Analyzes requirements, develops complex data models and ETL frameworks, supports testing and deployments, resolves data issues, and ensures compliance with enterprise standards. Leads engineering initiatives, architecture reviews, release activities, and technical design discussions while mentoring team members and collaborating with business and technology stakeholders.

Designs, develops, and leads enterprise-scale AWS and Azure data platforms, pipelines, lakes, warehouses, and lakehouses. Builds ETL/ELT workflows using SQL, Python, Spark, Databricks, Kafka, Airflow, and dbt; implements data quality, governance, security, monitoring, CI/CD, and automated testing. Optimizes performance, reliability, scalability, and cloud costs, troubleshoots production issues, collaborates with technical and business stakeholders, and mentors junior engineers.

Designs and maintains scalable data pipelines supporting analytics, AI, and enterprise data modernization. Responsibilities include coding in SQL, Python, Shell, and Terraform; implementing Snowflake and dbt solutions; ensuring data quality and governance; resolving complex data issues; leading agile delivery; mentoring engineers; collaborating across technical teams; and documenting solutions. The role requires strong cloud, database, orchestration, communication, and stakeholder leadership skills.

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

Lead the architecture, development, optimization, and operation of large-scale batch and real-time data platforms. Build Spark, Databricks, Kafka, and lakehouse pipelines handling multi-terabyte to petabyte-scale datasets. Optimize Spark and SQL performance, manage governance and monitoring, establish data-modeling and operational standards, and lead a global data engineering team through technical delivery, mentoring, stakeholder collaboration, and risk management.

Design, develop, and deploy AI-powered applications, agentic workflows, RAG solutions, and intelligent automation for regulated financial services. Build data pipelines, APIs, microservices, and production-grade AI components using Python, SQL, and cloud technologies. Integrate LLMs and machine learning models, ensure data quality and governance, collaborate with cross-functional teams, research emerging AI capabilities, and document technical implementations.

Build and maintain MLOps infrastructure for listener data and machine learning systems. Responsibilities include developing CI/CD pipelines, optimizing Apache Spark and Databricks workloads, productionizing models, managing scalable APIs and batch systems, implementing testing and monitoring, and supporting reproducible, cost-efficient cloud and containerized environments.

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.

Designs and delivers research data solutions, including REDCap projects, integrations, data models, and ETL pipelines across clinical and research systems. Manages data curation, harmonization, quality assurance, documentation, secure access, and privacy compliance. Translates stakeholder needs into technical requirements, leads complex projects, evaluates prototypes, troubleshoots issues, and communicates with researchers, clinicians, technical teams, and data custodians. The role also mentors colleagues and supports healthcare research and predictive-model initiatives.

Develop and support reliable data pipelines, data models, ETL/ELT processes, and cloud-based data solutions. Responsibilities include SQL development, automation, data quality monitoring, troubleshooting, source-to-target analysis, documentation, governance, testing, deployment, and collaboration with analysts, engineers, and business stakeholders.

Leads enterprise data architecture and strategy, designing scalable cloud data platforms, ETL/ELT pipelines, reusable data products, and integration frameworks. Establishes standards for data modeling, governance, security, quality, observability, testing, CI/CD, and infrastructure automation. Guides technology selection, platform modernization, resilience, and technical roadmaps while partnering with analytics, AI, and business teams. Provides senior technical leadership, conducts architecture reviews, mentors engineers, and communicates recommendations to organizational leaders.

Leads enterprise data architecture and strategy, designing scalable cloud data platforms, ETL/ELT pipelines, reusable data products, and governance frameworks. Ensures data quality, security, observability, resilience, and compliance while establishing engineering standards for modeling, testing, CI/CD, and infrastructure automation. Partners with business, analytics, AI, and technology teams, evaluates emerging technologies, guides architectural decisions, reduces technical debt, and mentors engineering teams.

Design, develop, and maintain Azure-based ETL/ELT pipelines and data platforms. Build transformations with Databricks, PySpark, and SQL; manage Azure SQL data models and performance tuning; execute on-premises-to-Azure migrations; deploy Azure applications; implement secure storage with ADLS Gen2 and Delta Lake; and monitor, troubleshoot, and optimize cloud data resources.

Lead and develop a team of data engineers building scalable real-time and batch pipelines and cloud data platforms. Set technical direction, manage major initiatives, and establish practices for architecture, testing, observability, reliability, and operational excellence. Partner with Product, Data Science, Analytics, and Engineering teams to deliver trusted data solutions supporting analytics, experimentation, machine learning, personalization, and high-volume marketplace experiences.

Participate in RBC’s summer innovation program by collaborating on a cross-functional team to research business challenges, design and build a fully functional data-driven prototype, troubleshoot solutions, and present recommendations to stakeholders. Responsibilities include data engineering, transformation, cleaning, mining, applying data science techniques, working with mentors and subject matter experts, continuous technical learning, and developing project strategy and communications.

Design, develop, and maintain scalable data pipelines and architectures for analytics and reporting. Perform ETL and streaming data integration, data modeling, governance, quality management, and workflow optimization across cloud and big data environments. Translate business requirements into data solutions, monitor reliability and performance, and collaborate with stakeholders to advance Walmart’s data strategy.

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 and deploy AI-powered applications, agentic workflows, and enterprise automation solutions. Build data pipelines, APIs, microservices, and AI components using Python, SQL, and related technologies. Integrate LLMs, machine learning models, RAG, embeddings, vector databases, and agent frameworks in a regulated financial-services environment. Collaborate with architects, product owners, data scientists, and engineers while documenting solutions and researching emerging AI capabilities.

Develop and maintain large-scale data and machine learning applications using Scala, Python, Spark, AWS, and modern DevOps practices. Responsibilities include designing data pipelines, deploying and monitoring machine learning models, optimizing distributed systems, supporting the full software lifecycle, documenting processes, and collaborating with data science, Data Ops, DevOps, and product teams in an Agile environment.

The Data Engineer will design, build, deploy, and maintain scalable data and software solutions across on-premise and cloud environments. Responsibilities include participating in the data engineering lifecycle, improving system performance and resilience, assessing cloud solutions, documenting architectures, collaborating with stakeholders and engineering teams, and implementing modern data practices using Azure, Databricks, Snowflake, Python, and SQL. The role also supports DevSecOps, CI/CD, infrastructure as code, and continuous improvement.

Supports data transformation initiatives by building and maintaining ETL/ELT pipelines, automating data ingestion, validating data quality, and transforming datasets using SQL or Python. Collaborates with business stakeholders and data professionals across multiple departments, while documenting workflows, data dictionaries, and processes. This role provides hands-on data engineering experience during an eight-month internship.