Senior Data Integration Developer, Fabric

Overview

At KPMG in Canada, our people bring their unique perspectives to Canada’s most important challenges. Here, you can build momentum that reaches beyond our business, develop skills for the future, and take ownership of your career with support at every stage. Join a firm where your career can make a difference.

The Senior Data Integration Developer (Fabric) designs, builds, and supports secure, scalable data integration solutions for KPMG Canada’s internal clients through the Enterprise Analytics Centre of Excellence (CoE). The role helps establish and operationalize Microsoft Fabric within a hybrid analytics ecosystem that includes the Enterprise Analytics Data Lake and Azure-native services.

The role combines hands-on data engineering with production reliability, platform governance, documentation, and stakeholder collaboration. Responsibilities span the design, deployment, monitoring, and optimization of data ingestion, transformation, orchestration, lakehouse, warehouse, and real-time solutions using Microsoft Fabric, OneLake, and related Azure services.

What you will do

Data engineering and modernization

• Design and implement end-to-end Fabric data engineering solutions for downstream reporting and analytics using Data Factory pipelines, Dataflows Gen2, notebooks and OneLake
• Build and maintain reliable batch and, where required, streaming data pipelines using Fabric Data Factory, Spark/PySpark, Eventstreams, and Real-Time Intelligence capabilities
• Develop extract, transform, load (ETL) and extract, load, transform (ELT) processes to ingest data from on-premises, cloud, API, file, database, and event-based sources into governed Fabric data stores
• Implement medallion architecture patterns across Bronze, Silver, and Gold layers, using Delta tables and reusable transformation frameworks
• Design and optimize Fabric OneLake solutions, SQL endpoints, data models, and curated data products for downstream reporting and analytics
• Support migration and modernization of legacy, on-premises, Azure Data Factory, Synapse, or other Azure data workloads into Microsoft Fabric where appropriate
• Develop and optimize SQL, Databricks (PySpark) and Python transformations to prepare, validate, and enrich data for analytics use cases

Production reliability and optimization

• Monitor capacity, pipelines, notebooks, refreshes, data quality, and workload performance. Diagnose failures and implement sustainable corrective actions

• Create operational runbooks, architecture diagrams, data flow documentation, support procedures, and technical standards for Fabric solutions

Security, governance, and delivery practices

• Apply source control, branching, automated testing, deployment pipelines, and release-management practices using Git or equivalent tooling

• Implement and operate security and governance controls, including workspace roles, least-privilege access, row-level security, sensitivity considerations, lineage, and Microsoft Purview integration

Collaboration and technical standards

• Collaborate with architects, analytics engineers, data analysts, business analysts, administrators, and business stakeholders to translate requirements into technical solutions that can be maintained and supported in production

• Contribute technical judgment through design reviews, platform standards, reusable assets, proof-of-concept projects, and continuous improvement of the Enterprise Analytics operating model

What you bring to the role

• University degree or college diploma in computer science, computer engineering, data engineering, information systems, mathematics, or a related discipline, or equivalent practical experience
• Four or more years of professional experience in data engineering, data integration, analytics engineering, business intelligence engineering, or a related field
• Hands-on experience with Microsoft Fabric and/or comparable Azure data technologies, including Azure Data Factory, Synapse Analytics, Azure Data Lake and Databricks
• Experience designing and supporting scalable data integration solutions, including ETL/ELT pipelines, data orchestration, and enterprise data platforms
• Strong SQL skills and practical experience with data warehousing, lakehouse architectures, dimensional modeling, and performance optimization
• Proficiency in Python and PySpark for data transformation, automation, and troubleshooting
• Experience working with Fabric components such as OneLake, Lakehouse, Warehouse, Data Factory, notebooks, and Delta tables
• Understanding of modern data architecture concepts, including Medallion architecture, data governance, metadata management, security, and lifecycle management
• Experience with Git-based source control, CI/CD practices, and deployment automation
• Ability to monitor, troubleshoot, and optimize production data solutions while supporting operational reliability
• Experience collaborating with business and technical stakeholders to translate requirements into scalable analytics solutions

KPMG Ontario Region Pay Range Information

The expected base salary range for this position is $87,500 to $130,500 and may be eligible for bonus awards. The determination of an applicant’s base salary within this range is based on the individual’s location, skills & competencies, and unique qualifications. In addition, KPMG offers a comprehensive and competitive Total Rewards program. 

Providing you with the support you need to be at your best

Our Values, The KPMG Way

Integrity, we do what is right | Excellence, we never stop learning and improving | Courage, we think and act boldly | Together, we respect each other and draw strength from our differences | For Better, we do what matters

KPMG in Canada is a proud equal opportunities employer and we are committed to creating a respectful, inclusive and barrier-free workplace that allows all of our people to reach their full potential. A diverse workforce is key to our success and we believe in bringing your whole self to work. We welcome all qualified candidates to apply and hope you will choose KPMG in Canada as your employer of choice.

Adjustments and accommodations throughout the recruitment process

AI Usage

We embrace the use of artificial intelligence (AI) to enhance the candidate experience and streamline our recruitment processes. AI tools may help with organizing applications or surfacing relevant qualifications. However, no hiring decisions are made using AI. Every hiring decision is made by our hiring managers and recruitment professionals, who are equipped with training that empowers them to use these tools responsibly. AI technologies used in our recruitment process undergo detailed risk assessments, including security and privacy requirements, that align with KPMG’s Trusted AI framework.

We believe technology should empower human judgment, not replace it. It’s one of the many ways we’re delivering on our vision of being a technology-first, people-driven firm.

Data engineering and modernization

• Design and implement end-to-end Fabric data engineering solutions for downstream reporting and analytics using Data Factory pipelines, Dataflows Gen2, notebooks and OneLake
• Build and maintain reliable batch and, where required, streaming data pipelines using Fabric Data Factory, Spark/PySpark, Eventstreams, and Real-Time Intelligence capabilities
• Develop extract, transform, load (ETL) and extract, load, transform (ELT) processes to ingest data from on-premises, cloud, API, file, database, and event-based sources into governed Fabric data stores
• Implement medallion architecture patterns across Bronze, Silver, and Gold layers, using Delta tables and reusable transformation frameworks
• Design and optimize Fabric OneLake solutions, SQL endpoints, data models, and curated data products for downstream reporting and analytics
• Support migration and modernization of legacy, on-premises, Azure Data Factory, Synapse, or other Azure data workloads into Microsoft Fabric where appropriate
• Develop and optimize SQL, Databricks (PySpark) and Python transformations to prepare, validate, and enrich data for analytics use cases

Production reliability and optimization

• Monitor capacity, pipelines, notebooks, refreshes, data quality, and workload performance. Diagnose failures and implement sustainable corrective actions

• Create operational runbooks, architecture diagrams, data flow documentation, support procedures, and technical standards for Fabric solutions

Security, governance, and delivery practices

• Apply source control, branching, automated testing, deployment pipelines, and release-management practices using Git or equivalent tooling

• Implement and operate security and governance controls, including workspace roles, least-privilege access, row-level security, sensitivity considerations, lineage, and Microsoft Purview integration

Collaboration and technical standards

• Collaborate with architects, analytics engineers, data analysts, business analysts, administrators, and business stakeholders to translate requirements into technical solutions that can be maintained and supported in production

• Contribute technical judgment through design reviews, platform standards, reusable assets, proof-of-concept projects, and continuous improvement of the Enterprise Analytics operating model

• University degree or college diploma in computer science, computer engineering, data engineering, information systems, mathematics, or a related discipline, or equivalent practical experience
• Four or more years of professional experience in data engineering, data integration, analytics engineering, business intelligence engineering, or a related field
• Hands-on experience with Microsoft Fabric and/or comparable Azure data technologies, including Azure Data Factory, Synapse Analytics, Azure Data Lake and Databricks
• Experience designing and supporting scalable data integration solutions, including ETL/ELT pipelines, data orchestration, and enterprise data platforms
• Strong SQL skills and practical experience with data warehousing, lakehouse architectures, dimensional modeling, and performance optimization
• Proficiency in Python and PySpark for data transformation, automation, and troubleshooting
• Experience working with Fabric components such as OneLake, Lakehouse, Warehouse, Data Factory, notebooks, and Delta tables
• Understanding of modern data architecture concepts, including Medallion architecture, data governance, metadata management, security, and lifecycle management
• Experience with Git-based source control, CI/CD practices, and deployment automation
• Ability to monitor, troubleshoot, and optimize production data solutions while supporting operational reliability
• Experience collaborating with business and technical stakeholders to translate requirements into scalable analytics solutions

KPMG Ontario Region Pay Range Information

The expected base salary range for this position is $87,500 to $130,500 and may be eligible for bonus awards. The determination of an applicant’s base salary within this range is based on the individual’s location, skills & competencies, and unique qualifications. In addition, KPMG offers a comprehensive and competitive Total Rewards program. 

Providing you with the support you need to be at your best