Top Data Engineer Jobs in Toronto

Build Python-based frameworks to validate, reconcile, cleanse, and transform large datasets. Use Pandas and NumPy to identify data quality issues, discrepancies, duplicates, and anomalies. Automate validation workflows, investigate root causes, produce data quality reports, optimize processing, and collaborate with business, analytics, and engineering teams on data migration, onboarding, and integration initiatives.

Build and operate Databricks-based data products and pipelines that create, model, and deliver syndicated and custom consumer audience segments. Develop tables, views, jobs, workflows, and real-time processing solutions using Python, SQL, Spark, and Delta Lake. Support marketplace delivery, reporting, observability, metadata, testing, CI/CD, privacy compliance, and AI-assisted development. Partner with data science and commercial teams to translate business requirements into scalable engineering solutions, while promoting architecture and engineering best practices.

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

Build and operate high-volume event streaming pipelines and analytical databases. Write production Elixir/Python services, integrate legacy pipelines, enforce event schemas and data quality, and provide monitoring, alerting, and SLA-driven observability for event data products.

Design, build, and maintain SparkSQL/PySpark data pipelines in the central data lake to ingest IoT, product, and unstructured data (video/audio). Produce reliable computed tables for analytics, model training, and dashboards; integrate external datasets; ensure high data quality and uptime; and collaborate with Data Science, ML, and cross-functional teams.

Participate in RBC’s summer innovation program as a Data Engineer, collaborating on a cross-functional team to research business challenges, design and build a functional prototype, transform and analyze data, and present solutions to stakeholders. Responsibilities include troubleshooting, applying data science and engineering techniques, working with mentors and subject matter experts, continuous learning, and contributing to strategy, communications, and executive presentations.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Design, build, and maintain scalable ETL/ELT pipelines and data architecture on Azure. Integrate and govern data sources, ensure quality and security, automate deployments, and collaborate with data scientists, analysts, and business teams to support analytics and operational use.

Build production ETL and ELT pipelines, CI/CD workflows, data ingestion systems, validation, monitoring, and reusable automation components. Apply Python, SQL, data modeling, warehousing, distributed processing, and cloud data services across Azure, AWS, or GCP. Collaborate with product managers, analysts, engineers, and stakeholders to translate business needs into scalable data solutions. Participate in agile development, optimize data performance and costs, and incorporate AI and automation into enterprise data workflows.

Develop and maintain large-scale data engineering systems, including data models, ETL frameworks, ingestion pipelines, metadata, lineage, governance, and data quality processes. Analyze complex data issues, support testing and deployments, lead architecture reviews, and ensure compliance with enterprise standards. The role also involves coordinating across technology teams, mentoring colleagues, managing technical risks, and contributing to data engineering best practices.

Develops and leads complex data engineering solutions, including data models, ETL frameworks, ingestion, data quality, metadata, governance, lineage, and lifecycle management. Performs technical analysis, resolves data issues, supports testing and deployments, participates in architecture reviews, and leads engineering initiatives. Partners across technology and business teams, ensures compliance with standards, mentors colleagues, and provides thought leadership in data engineering practices.

Develop and maintain large-scale data engineering frameworks, data models, ETL jobs, ingestion pipelines, metadata, lineage, and governance capabilities. Analyze complex data requirements, support testing and production deployments, resolve data issues, and communicate technical solutions to business partners. The role also leads design reviews, release activities, security practices, and data engineering initiatives while mentoring team members and collaborating across technology and business teams.

Develop and maintain data models, ETL jobs, ingestion frameworks, metadata, lineage, and data governance capabilities for a large-scale banking data platform. Analyze data requirements, resolve complex data issues, support testing and deployments, participate in architecture reviews, and ensure compliance with enterprise standards. Collaborate with technology and business teams, lead data engineering initiatives, and mentor colleagues while supporting production releases and risk management.

Build and maintain Betstamp’s real-time market data platform, including ingestion from 100+ sources, Redis Streams and ClickHouse pipelines, and APIs delivering sub-second odds data. Own feed latency benchmarking, integrations, normalization, reliability monitoring, and line-history infrastructure. Use AI coding tools responsibly while investigating performance and production issues. Work with senior engineers on architecture and high-impact changes. The role is based in Toronto or requires relocation, with an anticipated September 2026 start.

Build and maintain real-time sports betting and prediction-market data systems. Responsibilities include improving scraper ingestion across 100+ integrations, managing Redis Streams and ClickHouse pipelines, optimizing PostgreSQL and ClickHouse queries, maintaining schemas and migrations, and creating Prometheus/Grafana monitoring. The role also emphasizes AI-assisted development, debugging, testing, and investigation, with increasing ownership across the backend stack alongside senior engineers.

Design, build, and operate scalable batch, streaming, and CDC data pipelines across Snowflake and AWS. Lead ETL/ELT orchestration, data modeling, quality controls, monitoring, observability, incident response, and performance optimization. Develop production-grade SQL and Python solutions, establish engineering standards, and partner with analytics, data science, product, and engineering teams to deliver reliable data products. Provide technical guidance and independently solve complex data platform problems.

Design, develop, and support scalable Azure data pipelines and ETL solutions using Databricks, PySpark, SQL, and Azure Data Factory. Build reliable data ingestion and transformation processes, implement data quality and monitoring controls, troubleshoot production issues, optimize performance, and collaborate with business and technology stakeholders. Participate in code reviews, testing, documentation, Agile delivery, automation, and continuous improvement of enterprise data products.

Designs, builds, governs, and supports scalable analytics and data engineering solutions on Microsoft Fabric and Azure. Responsibilities include developing ingestion pipelines, lakehouse and warehouse models, orchestration, real-time solutions, performance optimization, testing, monitoring, incident resolution, security, capacity management, and platform governance. The role partners with business and technical stakeholders, facilitates design workshops, documents architecture, mentors teams, and contributes to estimation and enterprise BI strategy.

Design, build, and maintain Databricks/Spark ETL pipelines and Bronze-Silver-Gold architectures; implement event-driven streaming with Pub/Sub and Protocol Buffers; prepare datasets for LLM fine-tuning and embeddings; integrate third-party sources (Shopify, Klaviyo); implement attribution, CLV, and analytics models; ensure data quality, monitoring, and idempotent retry-safe pipelines.

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.

Design, build, and operate production AI systems and marketing data infrastructure. Develop retrieval pipelines, agent orchestration, CDP integrations, ETL/ELT pipelines, and analytics. Automate workflows with AI, ensure data quality and reliability, partner with stakeholders, ship Python and SQL code, mentor engineers, and drive requirements to production.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Lead the architecture and development of scalable data platforms and distributed processing systems. Establish enterprise data governance, quality, privacy, compliance, schema management, and lifecycle standards. Drive resilient, automated, and observable data pipeline strategies; evaluate technologies; review complex architectures; collaborate with business and technology stakeholders; and mentor data engineers in stream processing, data modeling, and MLOps.

Design, build, and maintain scalable data pipelines and storage (data lakes/warehouses). Integrate diverse sources, enforce data quality, security and compliance, optimize ETL/ELT, monitor pipelines, and mentor junior engineers while collaborating with data scientists, analysts, and stakeholders.