Data Engineer, Microsoft Fabric

Armstrong Collective•Vancouver, BC

1d•CA$105,000 - CA$125,000•Hybrid

About The Position

The position reports to Senior Manager, Data Operations. We are looking for a versatile Data Engineer with deep expertise across the Microsoft Fabric platform, spanning Real-Time Intelligence, Lakehouse, Spark engineering, Power BI semantic modelling, and enterprise data modelling. This is a hands-on individual contributor role focused on designing, building, and operating modern data solutions end-to-end. In this role, you will take ownership for delivering reliable, well-governed data products that support operational and strategic decision-making. You will apply sound judgment, collaborate closely with partners, and continuously develop your technical expertise while contributing to a high-performing data engineering team.

Requirements

5+ years of data engineering experience delivering both batch and streaming data solutions.
Strong expertise in data modelling, including normalized and dimensional modelling techniques.
Hands-on experience with Microsoft Fabric, including Real-Time Intelligence (Eventhouse/KQL) and Lakehouse/OneLake.
Strong proficiency in KQL (Kusto Query Language) and Spark (PySpark and Spark SQL).
Experience migrating complex SQL and T-SQL workloads to Spark-based platforms.
Experience building Power BI semantic models and reports, including Direct Lake and DirectQuery.
Familiarity with data governance and cataloguing using Microsoft Purview.
Strong problem-solving skills, attention to detail, and a continuous learning mindset.
Ability to communicate technical concepts clearly and collaborate effectively as an individual contributor.

Nice To Haves

Microsoft certifications such as DP-700 (Fabric Data Engineer Associate), DP-600 (Fabric Analytics Engineer), DP-203, or Databricks Data Engineer Associate / Professional.
Experience with Fabric IQ and Copilot-driven development across Fabric workloads.
Experience with Data Activator for event-driven actions and alerting.
Background implementing enterprise data governance frameworks (data quality, stewardship, lineage, role-based access) anchored in Purview.
Exposure to Data Mesh architectures, medallion architecture, and lakehouse design patterns in addition to dimensional modelling.
Experience working in regulated or data-sensitive industries (finance, healthcare, travel, etc.) where data quality and auditability are critical.

Responsibilities

Design and maintain canonical data models that serve as the foundation for analytics, reporting, and data products.
Build and operate scalable batch and streaming data pipelines using Microsoft Fabric Real-Time Intelligence and Lakehouse capabilities.
Develop, optimize, and maintain KQL queries, functions, update policies, and materialized views for real-time data processing.
Build and maintain Spark and PySpark pipelines using medallion architecture and Delta Lake best practices.
Modernize legacy T-SQL stored procedures and SQL workloads into Spark-based solutions, ensuring accuracy and maintainability.
Design and maintain Power BI semantic models aligned to enterprise data models and business requirements.
Apply data governance practices using Microsoft Purview, including cataloguing, lineage, classification, and responsible data handling.
Monitor data pipelines, proactively identify issues, and take ownership for data quality, reliability, and performance.
Collaborate with architects, analysts, and business stakeholders to translate requirements into scalable, well-modelled data solutions.
Contribute to data engineering standards, documentation, and continuous improvement initiatives.