Data Integration Engineer

Data Integration Engineer (Azure & Databricks) Location: Canada (Hybrid/Remote)Experience: 5–8 Years Position Overview We are seeking a hands-on Data Integration Engineer to join our growing data and analytics team. This role is focused on the design, development, enhancement, and support of enterprise data integration solutions within an Azure and Databricks ecosystem. The ideal candidate is an execution-oriented professional who enjoys building and supporting data pipelines, integrating data from multiple source systems, and implementing scalable modern data platform solutions. While the role requires participation in solution discussions, the primary focus is on delivery, implementation, and operational support, rather than enterprise architecture or strategic consulting. Key Responsibilities Data Integration & Engineering Design, develop, and maintain scalable data integration pipelines using Azure and Databricks. Build and support batch and near real-time data ingestion processes from multiple internal and external source systems. Develop data transformation logic to support analytics, reporting, and business consumption requirements. Implement data quality, validation, reconciliation, and monitoring processes. Optimize pipeline performance and troubleshoot production issues. Databricks Development Develop and maintain Databricks notebooks, workflows, and processing pipelines. Build transformation frameworks using Spark and Databricks best practices. Support data ingestion, cleansing, enrichment, and aggregation activities. Work with large and complex datasets across multiple domains. Azure Data Platform Delivery Develop solutions using Azure data services including: Azure Data Factory (ADF) Azure Data Lake Storage (ADLS) Azure Databricks Azure SQL Azure Synapse (preferred) Support deployment, monitoring, and operational activities across the data platform. Data Architecture Implementation Implement and support Medallion Architecture (Bronze, Silver, Gold layers). Ensure data lineage, governance, and consistency across the platform. Contribute to data modeling and solution design discussions. Translate architectural direction into technical implementation and delivery. Team Collaboration Collaborate closely with Data Engineers, Solution Architects, Product Owners, and Business Stakeholders. Support ongoing initiatives and enhancements within an established delivery team. Participate in Agile ceremonies, sprint planning, estimation, and backlog refinement. Assist in production support, troubleshooting, and continuous improvement initiatives. Required Qualifications 5–8 years of experience in Data Engineering, Data Integration, or Data Platform Development. Strong hands-on experience with Azure Databricks. Proven experience building and supporting enterprise-scale data pipelines. Strong understanding of data ingestion, transformation, and integration patterns. Experience integrating data from multiple source systems and platforms. Solid understanding of modern data lake and lakehouse architectures. Experience implementing Medallion Architecture concepts. Strong SQL development and data analysis skills. Experience working within Agile delivery teams. Technical Skills Required Azure Databricks Apache Spark / PySpark Azure Data Factory (ADF) Azure Data Lake Storage (ADLS) SQL Python Data Integration & ETL/ELT Development Data Quality & Reconciliation Preferred Azure Synapse Analytics Delta Lake CI/CD for Data Pipelines Azure DevOps Git Kafka or Event-Driven Architectures Power BI Preferred Experience Experience supporting cloud-based analytics and reporting platforms. Experience working with complex enterprise data ecosystems. Exposure to financial services, insurance, banking, or regulated industries. Experience supporting production environments and ongoing operational initiatives. Familiarity with data governance and data management best practices.

The Data Integration Engineer will design, develop, and maintain scalable data pipelines within an Azure and Databricks ecosystem. They are responsible for data ingestion, transformation, and ensuring data quality while collaborating with cross-functional teams to support analytics requirements.

Candidates must have 5–8 years of experience in data engineering with strong hands-on expertise in Azure Databricks and Spark. A solid understanding of modern data lake architectures, SQL development, and Agile delivery methodologies is required.

• Microsoft AzurePreferred
• SQLPreferred
• AgilePreferred
• PythonPreferred

Identified from the job description. Confirm important requirements above.

• Azure
• Databricks
• Data Engineering
• Apache Spark
• PySpark
• Azure Data Factory
• Azure Data Lake Storage
• SQL
• Python
• ETL
• ELT
• Data Integration
• Medallion Architecture
• Data Modeling
• Agile
• Data Quality
• Data Lakes
• Pipelines
• Azure Data Lake
• Solution Design
• Sprint Planning
• CI/CD
• Workflow Management
• Platform Design And Development
• Git (Version Control System)
• Azure Synapse Analytics
• Data Lineage
• Technology Ecosystems
• Azure Databricks
• Azure DevOps
• Agile Methodology
• Data Analysis
• Microsoft Azure
• Reconciliation
• Consulting
• Continuous Improvement Process
• Data Architecture
• Data Governance
• Extract Transform Load (ETL)
• Data Management
• Data Transformation
• Engineering Design Process
• Event-Driven Programming
• Financial Services
• Governance
• Scalability
• Enterprise Architecture
• Python (Programming Language)
• SQL Azure
• Program Process Monitoring

• Data & Analytics
• Technology
• Software
• Engineering
• Consulting
• Data Integration Engineer
• Integration Architect
• Software Developers

Minimum experience

5+ years

Apply by

Aug 21, 2026

Posting language

English

Working hours

40 hours per week