Principle Azure Data Solution Engineer

Microsoft Microsoft · Big Tech · Toronto, ON +1 · Digital Solution Engineering

This role is for an Azure Data Solution Engineer focused on designing and delivering modern, governed data platforms using Microsoft Fabric, Azure Databricks, and Azure SQL. The engineer will partner with customers to modernize their analytics and data estates, enabling them to adopt AI-ready Azure data solutions. The role involves guiding organizations through data platform modernization, designing and optimizing AI-ready analytics solutions, and translating technical capabilities into business outcomes. It emphasizes collaboration, technical sales, and deep expertise in Azure Data services to support AI workloads.

What you'd actually do

  1. Solid technical foundation designing and modernizing Cloud & AI solutions on Azure, partnering with customers to move from legacy environments to secure, scalable cloud native architectures
  2. Proficiency in designing and delivering modern, governed data platforms using Microsoft Fabric (OneLake, Lakehouse, DW, BI), Azure Databricks, Azure SQL and OSS databases, data engineering pipelines, and Purview—translating these capabilities into scalable, AI‑ready customer solutions. (L300 vs L400)
  3. Hands‑on experience modernizing data platforms and analytics workloads on Azure, including Lakehouse and modern data warehouse architectures, legacy EDW/Hadoop modernization, and migration to Fabric‑centric solutions.
  4. Ability to lead technical migration and modernization discussions, applying structured approaches (e.g., 6R strategy) to guide customer decisions.
  5. Drive technical sales by using technical demos, proof of concepts, technical architecture accelerator to influence solution design and enable deployments.

Skills

Required

  • Designing and delivering modern, governed data platforms using Microsoft Fabric (OneLake, Lakehouse, Data Warehouse, BI), Azure Databricks, and Azure SQL and OSS databases
  • Modernizing analytics and data estates
  • Designing and optimizing governed, AI-ready analytics solutions
  • Translating technical capabilities into measurable business outcomes
  • Designing secure, scalable, and resilient cloud data architecture
  • Guiding organizations through data platform modernization
  • Technical sales
  • Collaboration with customers, partners, engineering teams, and account stakeholders
  • Influencing technical decision makers
  • Understanding of hybrid and cloud native architectures
  • Networking fundamentals
  • Azure security and compliance principles
  • Hands-on technical mindset
  • Building and operating data engineering pipelines across Fabric and Databricks
  • End-to-end data architecture concepts (ingestion, storage, transformation, analytics, performance optimization, security, governance)

Nice to have

  • Deep subject-matter expertise in designing and delivering modern, governed data platforms
  • Partnering with customers to modernize analytics and data estates
  • Guiding organizations through data platform modernization
  • Translating technical capabilities into measurable business outcomes
  • Designing secure, scalable, and resilient cloud data architecture
  • Experience influencing technical decision makers
  • Structured approaches (e.g., 6R strategy)
  • Azure Databricks
  • Azure SQL
  • OSS databases
  • Purview
  • Lakehouse architectures
  • Modern data warehouse architectures
  • Legacy EDW/Hadoop modernization
  • Migration to Fabric-centric solutions
  • Batch and streaming ingestion
  • Data governance

What the JD emphasized

  • AI-ready Azure data solutions
  • AI-ready analytics solutions
  • AI-powered infrastructure
  • AI adoption
  • Cloud & AI adoption
  • AI driven business workloads
  • Microsoft Cloud & AI technologies
  • Cloud & AI solutions
  • AI workloads