Senior Data Engineer Azure Data Factory Developer (35968)
- Toronto, ON, Canada
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- Posting language
- English
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Original job description
Our client is seeking two Senior Data professionals to support a data engineering and integration initiative: one Data Engineer specializing in Databricks and Apache Spark, and one Azure Data Factory Developer focused on ETL and data integration. The engagement will include one onsite/hybrid resource in Toronto and one offshore resource, with both resources required to complete onboarding before the end of October 2026.
Role 1: Senior Data Engineer, Databricks / Apache Spark
Delivery Model: This role may be filled by either the onsite/hybrid Toronto resource or the offshore resource. Across the two positions, one resource will be onsite and one offshore.
Responsibilities
Delivery Model: This role may be filled by either the onsite/hybrid Toronto resource or the offshore resource. Across the two positions, one resource will be onsite and one offshore.
Responsibilities
Role 1: Senior Data Engineer, Databricks / Apache Spark
Delivery Model: This role may be filled by either the onsite/hybrid Toronto resource or the offshore resource. Across the two positions, one resource will be onsite and one offshore.
Responsibilities
- Design, develop and maintain data processing solutions using Databricks
- Develop and optimize Apache Spark jobs for data transformation and processing
- Build data ingestion and transformation workflows
- Develop scalable, reusable and unit-tested data engineering components
- Troubleshoot Databricks and Spark workloads
- Support deployment, issue resolution and enhancement activities
- Produce technical documentation and support knowledge transfer
- Strong Databricks experience
- Strong Apache Spark experience
- Data transformation and processing
- Data ingestion and pipeline development
Delivery Model: This role may be filled by either the onsite/hybrid Toronto resource or the offshore resource. Across the two positions, one resource will be onsite and one offshore.
Responsibilities
- Design, build and maintain Azure Data Factory pipelines and workflows
- Develop ETL and data integration solutions across source and target systems
- Support integrated processing and orchestration using Databricks where required
- Configure pipeline monitoring and error handling
- Troubleshoot and optimize data movement and pipeline execution
- Support testing, deployment and operational stabilization
- Produce technical documentation and support knowledge transfer
- Strong Azure Data Factory experience
- Working knowledge of Databricks
- Data integration experience
- ETL pipeline development and support
More about this job
Responsibilities
Design, develop, maintain, and optimize Databricks and Apache Spark data processing solutions, including ingestion and transformation workflows. Build and support Azure Data Factory ETL pipelines, troubleshoot and optimize data movement and execution, and assist with testing, deployment, documentation, and knowledge transfer.
Requirements
The roles require strong experience with Databricks and Apache Spark for the data engineering position, or strong Azure Data Factory experience for the developer position. Data transformation, ingestion, pipeline development, ETL, and data integration experience are required; working knowledge of Databricks is needed for the Azure Data Factory role.
Skills
- Databricks
- Apache Spark
- Azure Data Factory
- Data Transformation
- Data Processing
- Data Ingestion
- Data Pipeline Development
- ETL
- Data Integration
- Pipeline Monitoring
- Error Handling
- Troubleshooting
- Unit Testing
- Technical Documentation
- Knowledge Transfer
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Manufacturing
Keywords
- Databricks
- Apache Spark
- Azure Data Factory
- Data Engineering
- Data Integration
- ETL
- Data Transformation
- Data Processing
- Data Ingestion
- Data Pipelines
- Pipeline Orchestration
- Pipeline Monitoring
- Error Handling
- Unit Testing
- Testing
- Deployment
- Troubleshooting
- Operational Stabilization
- Technical Documentation
- Knowledge Transfer
- Source Systems
- Target Systems
- Manufacturing