Microsoft Fabric Data Engineer

Role overview

Key responsibilities

• Design scalable Fabric Lakehouse, Data Factory, pipeline and notebook solutions.
• Build reusable ingestion, transformation and medallion-layer patterns for governed data products.
• Develop AI Agents, Data Agents, ontology-driven solutions and RAG experiences.
• Create semantic models, relationships, calculations and DAX measures for analytics and AI.
• Develop production transformations, orchestration and validation with Python, PySpark and SQL.
• Automate semantic-model engineering, testing, documentation and operational tasks with Semantic Link Labs and Fabric Notebooks.
• Integrate data and AI into web applications, APIs and business workflows.
• Collaborate across architecture, platform, product and business teams through production deployment.
• Contribute standards, reusable accelerators, documentation and knowledge sharing.

Required technical capabilities

• Strong Microsoft Fabric data engineering experience across Lakehouse, pipelines, notebooks and OneLake.
• Python, PySpark and advanced SQL for processing and transformation.
• Semantic modeling, DAX, dimensional modeling and performance optimization.
• Knowledge of Agentic AI frameworks, RAG, embeddings, vector search and LLM integration.
• Hands-on understanding of AI Agents, Data Agents, ontology concepts and AI-ready semantic models.
• Working knowledge of Snowflake and Databricks.
• Git, CI/CD, REST APIs, security fundamentals and production deployment practices.

Qualifications and certifications

• Bachelor’s degree in computer science, data engineering, artificial intelligence or a related discipline, or equivalent relevant experience.
• Microsoft Certified: Fabric Analytics Engineer Associate (DP-600) is mandatory.
• Related certifications such as DP-203, AI-102 or PL-300 are preferred.
• 3–5 years of relevant data engineering, analytics engineering or AI solution development experience.

Preferred experience

• Experience developing AI-enabled web applications, APIs or conversational analytics.
• Experience with Azure AI services, Azure OpenAI or comparable enterprise LLM platforms.
• Production RAG or Agentic AI implementations with evaluation, observability and responsible AI controls.
• Experience with Databricks, Snowflake, Delta Lake and modern cloud data-platform integration.
• Experience with Direct Lake, real-time analytics, deployment pipelines or DevOps automation.

Behavioral competencies and success

Inclusion and belonging