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Clevanoo LLC logo

Data Engineer

Clevanoo LLC
Posted 7 hours ago
🇺🇸United States🏢Hybrid💰$90/hr📁Data & Analytics
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Principal Microsoft Data & AI Engineer

Location: Minneapolis, MN (Preferred) | Chandler, AZ | Irving, TX | Charlotte, NC

Work Model: Hybrid (3 Days Onsite / 2 Days Remote)

Duration: 18-Month Contract

Pay- $90/hr on W2

Must-Have Skills (Required)

Data Engineering & Database Technologies

This is fundamentally a Senior/Principal Data Engineering role with the highest emphasis on Microsoft SQL Server (40%) + Microsoft Fabric (30%) + Data Modeling (15%) + AI-assisted Analytics (10%) + Power BI/Excel (5%).


  • Microsoft SQL Server
  • Advanced SQL Development
  • Stored Procedures, Views, Functions, Query Optimization
  • ETL / ELT Development
  • Data Integration
  • Data Warehousing
  • Data Quality Management
  • Data Profiling, Validation & Reconciliation

Microsoft Fabric

  • Microsoft Fabric
  • Fabric Data Factory
  • Fabric Dataflows Gen2
  • Fabric Warehouse
  • Fabric Lakehouse
  • Fabric Semantic Models
  • Fabric Notebooks

Data Modeling

  • Relational Data Modeling
  • Dimensional Modeling
  • Analytical Data Modeling
  • Semantic Modeling
  • Enterprise Data Modeling
  • Repository-Driven Data Standards
  • Medallion Architecture (Bronze, Silver, Gold)

Business Intelligence & Reporting

  • Power BI
  • Semantic Models
  • DAX
  • Power Query
  • Executive Reporting
  • Dashboard Development

Advanced Excel

  • Power Query
  • Power Pivot
  • Data Models
  • Pivot Tables
  • Advanced Formulas
  • External Data Connections
  • Analytical Reporting

Artificial Intelligence (AI)

  • AI-Assisted Data Analysis
  • Prompt Engineering
  • Generative AI Tools
  • AI-Assisted SQL Development
  • AI-Assisted Documentation & Testing
  • Validation of AI-Generated Outputs
  • Responsible AI Usage

Strongly Preferred Skills

Microsoft Azure

  • Azure SQL
  • Azure Data Lake Storage (ADLS)
  • Azure Data Factory (ADF)
  • Azure Data Services

Databricks & Lakehouse Technologies

  • Azure Databricks
  • Delta Lake
  • Apache Spark
  • PySpark
  • Lakehouse Architecture

Automation & Low-Code Platforms

  • Power Apps
  • Power Automate

Programming & Scripting

  • Python
  • PySpark
  • PowerShell

DevOps & SDLC

  • GitHub
  • Azure DevOps
  • CI/CD Pipelines
  • Code Review
  • Release Management
  • Automated Testing
  • Production Support

Required Experience

  • 7+ years of Data Engineering, Analytics Engineering, Database Engineering, or Data Integration experience.
  • Expert-level Microsoft SQL Server and SQL development.
  • Experience building enterprise ETL/ELT pipelines.
  • Hands-on Microsoft Fabric implementation experience.
  • Experience designing governed enterprise data assets.
  • Experience integrating data from:
  • Enterprise databases
  • Excel files
  • CSV files
  • APIs
  • Email-delivered files
  • Exported reports
  • Web-based approved sources
  • Strong data governance and compliance experience.
  • Experience preparing curated datasets for:
  • Power BI
  • Excel
  • Executive Reporting
  • AI-Assisted Analytics

AI Experience Required

Candidates should demonstrate practical experience with:

  • Microsoft 365 Copilot
  • GitHub Copilot
  • Claude
  • VS Code AI Extensions
  • Enterprise LLM Platforms

Including:

  • Prompt Engineering
  • AI-Assisted SQL Generation
  • AI-Assisted Data Analysis
  • AI-Assisted Documentation
  • Prompt Template Development
  • AI Output Validation
  • Hallucination Detection
  • Data Leakage Risk Management

Key Responsibilities

  • Design and develop scalable data pipelines using Microsoft SQL Server and Microsoft Fabric.
  • Build reusable ETL/ELT solutions and governed data products.
  • Create analytical, dimensional, relational, and semantic data models.
  • Develop Power BI semantic models and reporting datasets.
  • Integrate structured, semi-structured, and unstructured data sources.
  • Optimize SQL performance and data processing workflows.
  • Perform data quality assessments and root cause analysis.
  • Implement Medallion architecture standards.
  • Support executive reporting and workforce analytics initiatives.
  • Leverage AI tools to accelerate data discovery, engineering, testing, and documentation.
  • Ensure compliance with data governance, privacy, security, and regulatory requirements.


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