Top Data Engineer Jobs in Toronto

Design, build, deploy, and maintain scalable multi-source ELT pipelines and enterprise data warehouse infrastructure. The role involves optimizing data systems for cost, performance, reliability, and SLAs; curating campaign and business data; creating monitoring and automation; and collaborating with engineering, analytics, SRE, and platform teams to deliver reliable data products.

Build and scale backend-focused software for Braze’s petabyte-scale Data Lakehouse. Develop data pipelines, asynchronous processing, ETL applications, and end-to-end features across platforms such as Snowflake, ClickHouse, and Databricks. Drive architectural decisions, contribute to technical strategy, and deliver scalable web applications handling high volumes of data and traffic in an agile environment.

Build and maintain backend-focused, end-to-end software for Braze’s petabyte-scale Data Lakehouse platform. Develop infrastructure and tools supporting Snowflake, ClickHouse, and Databricks, manage high-volume event data, and deliver front-end experiences when needed. Collaborate across teams, apply strong software design and algorithms knowledge, and help scale systems handling billions of daily rows and significant customer and internal workloads.

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.

Design and build cloud-native microservices, REST APIs, and highly available backend services supporting large-scale data requests. Translate business requirements into scalable platform solutions, contribute to architecture modernization, and uphold engineering practices including testing, observability, CI/CD, and code reviews. Troubleshoot production issues, improve reliability, and evaluate emerging data engineering and generative AI technologies. Mentor colleagues and collaborate across technical and business teams.

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.

Develop and maintain complex data models, ETL jobs, ingestion processes, metadata, lineage, and data governance solutions. Analyze business and data requirements, investigate data quality issues, support testing and releases, and collaborate with technical and business stakeholders. The role also involves code reviews, CI/CD support, secure coding, risk management, mentoring, and leading data engineering initiatives using Java/PySpark, Python, SQL, Azure data services, and source-control tools.

Coordinate automation solutions and web applications using large-scale data. Analyze and transform structured and unstructured data, develop ETL workflows and portals, improve Finance reporting, and collaborate with stakeholders across departments. The role requires Python, SQL, data-processing tools, web development, advanced Excel and PowerPoint, with opportunities to use GitHub Actions and Tableau.

Design and maintain people analytics data pipelines, models, semantic layers, and Power BI dashboards. Develop predictive workforce analytics, forecasting, talent segmentation, and organizational health metrics. Partner with HR, technology, business leaders, and vendors to improve data quality, governance, privacy, and reporting. Translate workforce trends into strategic insights and compelling storytelling for planning, talent management, DEI, compliance, and executive decision-making.

Lead the design, development, deployment, and support of enterprise data pipelines, reusable frameworks, and governed lakehouse solutions on Azure Databricks. Build batch and streaming workloads using Spark, PySpark, SQL, Delta Lake, and related technologies. Establish engineering standards, automated testing, CI/CD, monitoring, security, and performance optimization. Partner with architecture, governance, security, business, vendors, and delivery teams while resolving production issues and modernizing legacy ETL platforms.

Build and own production data pipelines, dbt models, semantic layers, and supporting infrastructure across Snowflake, BigQuery, Airflow, Fivetran, Databricks, AWS, and GCP. Partner with stakeholders across business functions to scope requirements, resolve data quality issues, monitor SLAs, support incident response, and apply data governance practices. The role requires autonomous delivery, strong communication, production-grade Python and SQL, and contributions to engineering standards, CI/CD, testing, and documentation.

Designs and maintains data pipelines and ETL processes in Microsoft Fabric, ensuring data quality, governance, warehouse performance, and reliable migration of data assets. Collaborates with stakeholders to develop scalable cloud data solutions, resolves pipeline issues, and supports analytics and reporting needs.

Develops, deploys, and monitors production data and machine learning pipelines. Responsibilities include data preparation, exploratory analysis, predictive modeling, feature and algorithm selection, model deployment, infrastructure development, and monitoring model performance, data quality, drift, latency, and reliability. The role also requires communicating insights, collaborating cross-functionally, and translating business problems into scalable data and ML solutions.

Build and scale data platforms, ETL pipelines, data lakes, warehouses, feature stores, and orchestration tooling. Integrate clickstream, relational, and unstructured data using Databricks and AWS. Partner with data scientists, ML engineers, product managers, and business stakeholders to support analytics, predictive modeling, and data-driven decisions. Maintain production systems, automate testing and monitoring, and communicate technical roadmaps and challenges.

Develops and supports ETL applications and data pipelines across legacy and modern platforms. Responsibilities include systems analysis, design, construction, testing, implementation, operational support, troubleshooting ETL processes and SQL workloads, performance tuning, and continuous improvement. The role requires expertise in ETL architecture, data warehousing, Unix scripting, Oracle or MySQL, application integration, and database development, with exposure to streaming technologies and modern languages such as Spark, Scala, Java, or Python.

Provides technical leadership for a scalable data engineering platform on Databricks. Designs reusable frameworks, production pipelines, engineering standards, monitoring, and operational practices using Python, SQL, and Spark. Partners with Architecture, Data Science, ML Engineering, Product, and technology teams to integrate data capabilities, translate commercial real estate requirements, mentor engineers, conduct code reviews, and influence cross-team technical direction.

Build and maintain production data pipelines using Airflow and BigQuery; design data models, optimize warehouse queries, monitor reliability, and deliver custom data solutions. Collaborate with analysts, product, customer success, and stakeholders to translate requirements into production systems. Improve data infrastructure and workflows while using AI tools to accelerate development, debugging, design, and documentation.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

Designs, develops, tests, and deploys high-throughput data ingestion, integration, curation, and ETL pipelines using Databricks, Scala, and PySpark. Builds near-real-time streaming, event-driven, and batch solutions on Azure or AWS, including data lakehouse architectures. Optimizes Databricks performance, troubleshoots DevOps and environment issues, performs root-cause analysis, and supports the full software development lifecycle. The role is onsite four days per week.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Build and maintain cloud-based ELT/ETL pipelines using dbt, Airflow, and Cloud Composer. Implement data transformations, ingestion workflows, quality tests, monitoring, and troubleshooting. Maintain schemas and lakehouse structures across Snowflake, BigQuery, and Databricks. Document pipelines and collaborate with analytics and data science teams to deliver optimized, reliable data structures.

Develops complex data models, ETL jobs, and data engineering frameworks using Azure, Databricks, PySpark, Python, SQL, and ADF. Analyzes data requirements, supports ingestion and data quality, maintains governance, metadata, and lineage, and resolves complex data issues. Leads design reviews, testing, deployment, release activities, and engineering initiatives while collaborating with technical and business stakeholders and mentoring team members.

Supports development, maintenance, and troubleshooting of enterprise data pipelines, including ingestion, transformation, validation, data modeling, and source-system integration. Works with Snowflake and cloud data platforms to improve data quality, documentation, monitoring, and operational processes. Collaborates with engineers, architects, QA, analysts, and business stakeholders while developing skills in SQL, ETL/ELT, orchestration, data warehousing, testing, and disciplined software delivery.

Develops, operates, troubleshoots, and modernizes enterprise data integrations using Talend. Responsibilities include building ETL/ELT pipelines, APIs, batch and real-time jobs, SQL transformations, scheduling, monitoring, production support, incident resolution, data quality, root-cause analysis, and platform maintenance. The role collaborates with technical and business stakeholders across development, testing, and production environments, while applying AI and automation to improve pipeline development, testing, monitoring, and documentation.

Lead design and build scalable data architectures, ETL pipelines, ingestion and data quality frameworks. Collaborate with cross-functional teams, enforce data governance, analyze data for insights, mentor junior engineers, and serve as the technical authority on data engineering best practices.