Sr Data Engineer To Support The Implementation Of An End To End Analytics Solution Using Micro [...]

Our valued client is looking for a Sr. Data Engineer to support the implementation of an end‑to‑end analytics solution using Microsoft Fabric to ingest data from various sources, create semantic models, and enable dashboard/visualization creation for the business team.

Annual Salary approximately from $94,580.00 – $111,320.00 per year.

This is a hybrid position (2 days/week on site) in Ottawa.

Tasks

• Implement an end‑to‑end analytics solution using Microsoft Fabric to ingest data from various sources, create semantic models, and enable dashboard/visualization creation for the business team.
• Assist the data management team in implementing data governance frameworks, policies, and controls including classification, sensitivity, access controls, and quality validation.
• Design and implement data pipelines in Microsoft Fabric, including source mapping and documentation; create and optimize semantic models including defining business entities, relationships, and security requirements.
• Set up development, testing, and production environments.
• Create comprehensive technical documentation covering configurations, data models and business rules.

Prior Experience

• Must be eligible for or possess a reliability clearance.
• Data modeling methodologies (Star schema, Snowflake schema; Data Vault is an asset).
• End‑to‑end data pipeline development, optimization, and monitoring.
• Data quality frameworks, validation techniques, and data profiling.
• Database platforms (SQL Server, Azure SQL; Oracle is an asset).
• Fabric/PowerBI semantic model development and DAX optimization.
• Multi‑platform data integration and ETL/ELT design patterns.
• Lakehouse and Medallion Architecture implementation.
• Data lineage documentation and data mapping.
• Technical documentation and solution design.
• Applying organizational policies, procedures, and data governance regulations.

Technical Skills

• Microsoft Fabric (Data Factory, data pipelines, lakehouses, warehouses, notebooks, Spark job definitions).
• PowerBI (semantic modeling, DAX, Power Query M, dataflows).
• SQL (advanced T‑SQL, stored procedures, query optimization).
• Spark (PySpark, Spark SQL, Delta Lake).
• Python for data engineering and transformation.
• ETL/ELT design patterns and best practices.
• Lakehouse and Medallion Architecture (Bronze/Silver/Gold).
• Azure data services (Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure SQL).
• Version control (Git) and Azure DevOps CI/CD.
• SSIS, SSRS, SSMS (an asset).