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# Microsoft Fabric Sr. Data Engineer

- **Company:** 2020 Companies
- **Location:** Atlanta, GA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Business Intelligence Development, Continuous Integration, Information Engineering, Data Structures, Dimensional Modeling, Github, Power BI, Technical Debt, Microsoft Fabric, Data Lakes
- **Published:** May 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=4e6ff5805e95f198

## About the Role

This role is highly visible and impact-driven, with direct influence on client satisfaction, retention, and growth. Ideal candidates bring strong SQL, Delta Lake, and Fabric experience, along with the ability to communicate complex ideas clearly., Required

 * 4-7 years of data engineering experience
 * Strong SQL and hands-on Delta Lake experience
 * Microsoft Fabric experience that you've built, not just read about
 * Dimensional modeling you can defend in a design review
 * Power BI semantic model optimization experience
 * Experience building scalable ingestion frameworks
 * A track record in client-facing or revenue-driven analytics environments
 * Critical thinking skills
 * Exceptional communication skills, written and verbal, that translate complex technical decisions for non-technical audiences
 * A growth mindset, you're energized by hard problems, not slowed down by them, strong team player

Preferred

 * dbt experience
 * GitHub-based development workflows and CI/CD experience
 * Agile mindset and development experience

## Description

Build Scalable Client Data Pipelines for scale from day one

 * Develop and maintain Microsoft Fabric pipelines and notebooks
 * Implement incremental loads, watermarking, and merge logic (Delta Lake)
 * Design ingestion patterns that support rapid onboarding of new clients
 * Engineer monitoring and observability to prevent SLA breaches while catching problems before clients do

Design Lakehouse Models for Growth

 * Implement Medallion architecture (Bronze/Silver/Gold)
 * Build dimensional models for revenue-focused analytics
 * Design reusable, scalable data structures across multiple clients
 * Design data structures that don't become someone else's technical debt
 * Optimize partitioning and storage for predictable performance

Optimize Power BI Performance that Clients Actually Notice

 * Improve Direct Lake and semantic model performance in Power BI
 * Reduce refresh times and memory usage
 * Partner with BI developers to maintain high modeling standards

Improve Platform Efficiency That Protects Margin

 * Monitor Fabric capacity usage and workload patterns
 * Optimize refresh strategies and reduce compute spikes
 * Contribute to GitHub workflow and future dbt integration roadmap

Your efforts will have a direct influence on:

 * Time-to-market for new client dashboards
 * Client satisfaction and retention
 * SLA performance and reporting reliability
 * Analytics delivery margins through efficient capacity use


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