Top Data Engineer Jobs in Toronto

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Lead event-driven and batch processing decisions, establish standards for observability, freshness, and failure handling, and guide cross-functional platform design. Mentor engineers, conduct design reviews, influence teams without direct authority, and drive foundational platform improvements. The role requires deep Python and SQL expertise, cloud data warehouse experience, scalable API-based ETL design, orchestration, and containerized infrastructure experience.

Build and maintain reliable data pipelines, integrations, APIs, and event-driven or batch processing systems. Transform data from advertising, commerce, and order management platforms while improving monitoring, data freshness, performance, security, and scalability. Partner with product, infrastructure, and data engineering teams to expand the integration platform and onboard new sources in a cloud-native, multi-tenant environment.

Build and operate large-scale infrastructure and data pipeline platforms. Responsibilities include developing automation, monitoring system health, improving reliability and scalability, managing cloud infrastructure, participating in design and production readiness reviews, planning capacity, documenting processes, and responding to incidents through on-call rotations. The role requires production software development, infrastructure tooling, data streaming and storage expertise, and experience optimizing distributed systems in public cloud environments.

Design, build, and maintain scalable data platforms, architectures, and pipelines using Azure Fabric, Databricks, and Snowflake. Develop batch and real-time ingestion frameworks, optimize data workflows, perform ETL/ELT and data modeling, and maintain production-grade transformation layers. Ensure data security, compliance, reliability, and performance while collaborating with analysts, scientists, engineers, and nontechnical stakeholders to deliver effective data solutions.

Develop and maintain complex software components for a data security project. Responsibilities include analyzing requirements, designing and coding solutions, testing, debugging, reviewing code and designs, supporting production, resolving application issues, and guiding development activities to ensure quality and timely delivery.

Leads enterprise DataOps and data platform engineering strategy, defining cloud infrastructure architecture, Terraform standards, CI/CD practices, governance, observability, and operational resilience. Drives multi-team platform transformations, mentors senior engineers, manages technical alignment with executive stakeholders, and develops AI-enabled automation using Cursor, Sana, and Gemini. Establishes data product, DataOps, and active governance methodologies across Workday’s enterprise data ecosystem.

Design, build, and operate a unified data platform: ingesting, processing, and exposing large volumes of data. Develop scalable ingestion pipelines, storage layers, observability, SLIs/SLOs, multi-tenant models, and metadata/ governance to enable secure cross-product data access and migrations from legacy systems.

Owns end-to-end data infrastructure, including secure data lakes, streaming applications, data warehouse systems, replication and change data capture pipelines. Responsibilities include architecture, deployment, maintenance, performance optimization, security controls, documentation, operational runbooks, and cross-functional partnership with analytics and product teams. The role requires strategic data infrastructure thinking and production experience in SaaS or data-heavy environments.

Build and operate investment data pipelines, integrations, and Snowflake platform capabilities supporting Aladdin and legacy systems. Responsibilities include ETL/ELT development, data modeling, orchestration, quality controls, lineage, testing, CI/CD, infrastructure as code, and production operability. The role also owns migration and conversion initiatives, resolves data issues, documents current and target-state platforms, and collaborates with investment, risk, finance, architecture, and delivery teams across Agile and waterfall environments.

Design, build, and optimize ETL pipelines, data schemas, and data tools to support analytics and data science. Tune SQL and databases for performance and collaborate with teams to scale data infrastructure using Snowflake, Python, and related tools.

Build and operate reliable data pipelines transforming flight test, simulation, and operational data into versioned, traceable ML training datasets. Responsibilities include ingestion, normalization, time alignment, validation, dataset lineage, anomaly mining, labeling workflows, deployment feedback loops, monitoring, and data coverage analysis. Collaborate with flight test and operations teams on instrumentation and data access. The role is on-site in Boston and requires production data engineering experience supporting machine learning pipelines.

Design, build, and operate scalable data ingestion, replication, and lakehouse infrastructure to move petabytes of data into a Delta Lake on S3. Improve reliability, observability, security, and developer experience for Spark/Databricks processing. Develop internal libraries and tooling (Go/Python), collaborate with cross-functional teams, and help shape long-term data platform and AI-ready infrastructure.

Design, build, and operate large-scale Spark/PySpark data pipelines and the enterprise data platform. Implement data integrations, transformations (Python/SQL), monitoring, and data quality. Build MCP servers and AI-enabled tooling, optimize performance, mentor engineers, and lead cross-functional data projects to enable analytics and automation.

Lead end-to-end data and ML pipelines for onboarding and fraud decisions: ingest streaming and batch sources, build feature platform and entity resolution, productionize ML models, enforce PII/data governance, and set technical direction while mentoring engineers and ensuring low-latency, reliable serving.

Design and own scalable data pipelines, warehousing architecture, and dbt transformation layers from ingestion through analytics and incentive payouts. Lead data-platform architecture, including OLake, Iceberg, and Snowflake workflows. Establish performance, cost, quality, lineage, observability, and schema-drift standards. Partner with engineering and analysts on telematics accuracy and driver incentive reporting while using AI coding tools to accelerate development.

Own production-grade analytics pipelines from design through maintenance, build dbt-based analytical models using Kimball dimensional modeling, and deliver reliable data features. Partner with Product, Engineering, and Data Science teams to translate retail signals into business insights. Establish standards for monitoring, documentation, reproducibility, and code quality. Use AI tools to accelerate development and prototype conversational data experiences for natural-language retail data exploration.

Own and scale the company’s analytics infrastructure, including BigQuery data warehouses, dbt data marts, external data exchange, DataOps, data quality, governance, security, and cloud cost optimization. Partner with application database teams, analytics engineers, and data scientists to enable reliable metrics and scalable client data delivery.

The Guidewire Data Engineer will design schemas in Snowflake, develop ETL processes, optimize SQL queries, and integrate Guidewire with data platforms.

Lead design and implementation of scalable data models, ETL/ELT pipelines and migrations to Snowflake/Databricks. Optimize data workflows, ensure data quality and governance, support analytics reporting, and provide technical leadership to junior engineers while collaborating with stakeholders to deliver data-driven solutions.

Design, build and optimize BI assets (QuickSight dashboards, semantic data models) on SQL datasets. Develop and maintain data pipelines, perform SQL tuning, ensure data quality and governance, and support the web presentation layer. Collaborate with business partners to deliver accurate, performant, secure reporting and analytics.

Develop and productionize data science and engineering solutions for fraud detection, anti-money laundering, and claims automation. Responsibilities include building data pipelines into SQL and Elasticsearch, developing and tuning machine learning models, automating business processes in C#, working with structured and unstructured data, and engaging clients to gather feedback, present solutions, and define product roadmaps.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Partner with Finance, Engineering, Sales, Product, and Marketing to deliver reliable data for reporting and decision-making. Improve manual workflows, validate data accuracy, and design scalable solutions using modern data modeling practices. Support financial and revenue-related analytics, including ARR and revenue recognition, while balancing immediate fixes with long-term architecture.

Build and register domain-grounded data agents over governed Databricks and Snowflake datasets. Perform lakehouse migrations to open formats such as Apache Iceberg, curate catalogue metadata, and support data-contract workflows. Partner with business stakeholders and subject-matter experts to validate data assets. Implement advanced SQL and PySpark pipelines, orchestration, data quality, observability, lineage, masking, and row- and column-level security. The role also supports vector search, embeddings, cloud data onboarding, and regulated-domain use cases.

Design, build, and manage Northland's Azure-based enterprise data platform. Lead data architecture, develop scalable ETL/ELT pipelines, model enterprise data, ensure data quality and governance, deliver self-service data marts and Power BI datasets, and provide technical leadership to support analytics and AI initiatives.

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.