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Strategic Staffing Solutions · Minneapolis, MN

Principal MS Data & AI Engineer

Hybriddirectorcontract$197,600 – $208,000 / yearPosted today
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Principal Microsoft Data & AI Engineer

Client: Wells Fargo (MRS)

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

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

Duration: 18-Month Contract

Pay- $95-$100/hr

SORRY NO CTC'S OR OPT'S!

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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