Experience: 3+ years, Microsoft Fabric, SQL, Python, PySpark
Job Description & Details
"The data landscape is rapidly evolving, and Microsoft Fabric is emerging as a unified analytics platform that bridges lakehouse and data warehousing capabilities. Professionals who master Fabric are in high demand as companies look to modernize their data stacks. This Fabric Data Engineer role offers a chance to shape cutting\u2011edge data solutions in Seattle's vibrant tech scene.\n\n# Job Summary\nWe are seeking a Fabric Data Engineer to design, build, and maintain modern data solutions using Microsoft Fabric (Lakehouse, Warehouse, Semantic Models). The role involves data modeling, lakehouse architecture, governance, and optionally working with knowledge graphs to enable self\u2011service analytics.\n\n# Top 3 Critical Skills Table\n| Skill | Why it's critical | Mastery Level |\n|---|---|---|\n| Microsoft Fabric (Lakehouse, Warehouse, Semantic Models) | Core platform for unified analytics; drives all data ingestion, storage, and reporting. | Senior |\n| Data Modeling & Lakehouse Architecture | Determines data reliability, performance, and scalability across analytics workloads. | Mid |\n| Data Governance & Metadata Management | Ensures data quality, compliance, and discoverability for enterprise users. | Senior |\n\n# Interview Preparation\n1. **How do you design a lakehouse schema in Microsoft Fabric to support both batch and real\u2011time analytics?**\n *What the interviewer is looking for:* Understanding of partitioning, delta tables, and semantic model layering.\n2. **Explain the differences between Fabric Warehouse and Fabric Lakehouse. When would you choose one over the other?**\n *What the interviewer is looking for:* Knowledge of performance trade\u2011offs, cost, and use\u2011case suitability.\n3. **Walk me through implementing data governance in Fabric. Which metadata features do you use?**\n *What the interviewer is looking for:* Experience with Fabric's data catalog, lineage, and security policies.\n4. **How would you integrate PySpark jobs with Fabric's Lakehouse to transform raw data?**\n *What the interviewer is looking for:* Practical steps for Spark session creation, connector usage, and job orchestration.\n5. **Describe a scenario where you used knowledge graphs or ontology concepts to improve data discoverability.**\n *What the interviewer is looking for:* Ability to translate semantic relationships into actionable data assets.\n\n# Resume Optimization\n- Microsoft Fabric\n- Lakehouse Architecture\n- Data Warehouse\n- Semantic Models\n- SQL\n- Python\n- PySpark\n- Data Modeling\n- Data Governance\n- Metadata Management\n\n# Application Strategy\nWhen reaching out to the recruiter, send a concise email that starts with a friendly greeting, attaches your resume, and clearly maps your experience to the role. Highlight your top skills such as Microsoft Fabric and data governance, reference any relevant projects (e.g., building a lakehouse solution), and explicitly mention the specific qualifications listed in the job description.\n\n# Career Roadmap\n| Current Role | Typical Experience | Core Focus | Next Position |\n|---|---|---|---|\n| Fabric Data Engineer | 2\u20114 years in data platforms | Build lakehouse, governance, analytics | Senior Fabric Data Engineer |\n| Senior Fabric Data Engineer | 4\u20116 years, lead projects | Architecture, mentorship, advanced analytics | Data Platform Lead |\n| Data Platform Lead | 6\u20119 years, cross\u2011team ownership | Strategy, roadmap, stakeholder alignment | Director of Data Engineering |\n| Director of Data Engineering | 9+ years, executive leadership | Organizational data vision, budgeting, innovation | VP of Data & Analytics |\n"