Intermediate to Senior Data Engineer to build scalable Databricks pipelines (PySpark, SQL, medallion architecture) on Azure, enabling automated, self service Power BI reporting for a leading North American utility’s data and analytics transformation.

Intermediate to Senior Data Engineer (Databricks) 6 month contract, likely extension | Remote, Canada (EST hours) | 40 hrs/week About the Project Our client, a leading North American utility, is transforming its data and analytics function. The team is moving away from ad hoc, ticket based reporting toward a scalable data platform that delivers automated, self service reporting in Power BI. You’ll help build out close to 100 business metrics across a layered (medallion) architecture in Databricks, with more to follow as the platform grows. The team recently adopted Agile with 2 week sprints. Responsibilities Design, build, and maintain data pipelines in Databricks using PySpark and SQL Develop and organize data across bronze, silver, and gold layers (medallion architecture) Translate business requirements into reliable, reusable data models and metrics Enable automated reporting and self service analytics in Power BI Ingest and integrate data from multiple sources, including SAP and Azure services Improve data quality, performance, and pipeline reliability Work within Agile sprints alongside data architects, analysts, and business partners Must Haves 5 to 10+ years of data engineering experience Strong hands on experience with Databricks PySpark and Databricks SQL Medallion architecture experience Nice to Haves Utility industry experience (billing, accounts receivable, customer operations, call centre, outage management, ADMS, or meter/premise data) SAP data experience (functional or technical)



Python Experience in an Azure/Microsoft environment Intermediate to Senior Data Engineer (Databricks) 6 month contract, likely extension | Remote, Canada (EST hours) | 40 hrs/week About the Project Our client, a leading North American utility, is transforming its data and analytics function. The team is moving away from ad hoc, ticket based reporting toward a scalable data platform that delivers automated, self service reporting in Power BI. You’ll help build out close to 100 business metrics across a layered (medallion) architecture in Databricks, with more to follow as the platform grows. The team recently adopted Agile with 2 week sprints. Responsibilities Design, build, and maintain data pipelines in Databricks using PySpark and SQL Develop and organize data across bronze, silver, and gold layers (medallion architecture) Translate business requirements into reliable, reusable data models and metrics Enable automated reporting and self service analytics in Power BI Ingest and integrate data from multiple sources, including SAP and Azure services Improve data quality, performance, and pipeline reliability Work within Agile sprints alongside data architects, analysts, and business partners Must Haves 5 to 10+ years of data engineering experience Robust hands on experience with Databricks PySpark and Databricks SQL Medallion architecture experience Nice to Haves Utility industry experience (billing, accounts receivable, customer operations, call centre, outage management, ADMS, or meter/premise data) SAP data experience (functional or technical) Python Microsoft Fabric, Azure Data Factory, Azure Functions, Dataflows Experience in an Azure/Microsoft environment Power BI exposure Disclaimer: AI may be used in evaluating candidates. This posting is for an existing vacancy.

📌 Intermediate to Senior Data Engineer to build scalable Databricks pipelines (Toronto)
🏢 S.I. Systems
📍 Toronto