LGM is a national leader in providing warranty, finance and insurance services to the Canadian automotive industry. Since 1998, LGM has partnered with leading automotive manufacturers and dealerships across Canada to deliver award-winning F&I solutions. Dealer partnerships are complemented with the strong backing and support of their automotive manufacturing brands, which include BMW/MINI, Kia, Mazda, Volvo, Jaguar/Land Rover, Mitsubishi Motors, Polestar and Motorrad.
The Data Engineer will play a key role in modernizing and scaling LGM’s data platform using Microsoft Fabric and Azure within our Business Intelligence team.
Develop, optimize, and operate resilient data pipelines for ingestion, replication/CDC, and transformation across cloud and on-Prem sources, leveraging Microsoft Fabric and/or Azure data services to meet defined SLAs (batch and near real-time). ‑prem sources, leveraging Microsoft Fabric and/or Azure data services to meet defined SLAs (batch and near real-time)
Design, test, and implement dimensional data models (star schemas, conformed dimensions, fact tables, aggregates) to support enterprise reporting, self-service analytics, and governed metric definitions.
SQL engineering and performance optimization across relational platforms (e.g., SQL Server/Azure SQL/Fabric Warehouse SQL), including query tuning, troubleshooting production issues, and improving data structures for reliability and scale.
Enable analytics and reporting by publishing curated datasets and semantic models, supporting Power BI development best practices (performance, incremental refresh patterns, RLS/OLS, reusable measures) and contributing to migration away from legacy reporting (e.g., SSRS) where applicable
Ingest and curate semi-structured and unstructured data (JSON, APIs, logs, files), managing schema evolution, validation, and scalable storage formats.
Collaborate on enterprise data governance: data definitions, data contracts, documentation, cataloging, and stewardship practices to ensure consistency and trusted data across domains
Strong experience in data engineering, ETL/ELT, and data warehousing, including dimensional modeling and delivering curated data marts/data products.
Advanced T‑SQL (Transact‑SQL) skills for development, troubleshooting, and maintenance of legacy ETL processes and data pipelines (e.g., SSIS/SQL Server–based workloads).
Experience with Microsoft cloud data platforms, with preference for Microsoft Fabric and/or Azure services such as OneLake, Lakehouse/Warehouse, Data Pipelines, Dataflows Gen2, Notebooks/Spark, Mirroring
Experience enabling Power BI at scale, including semantic model fundamentals (measures, relationships, performance patterns) and governance practices (certification, shared datasets, workspace standards). SSRS experience. Ability to think creatively.
