Lead Analytics Solutions Engineer

University of Arkansas, Fayetteville

Fayetteville, Arkansas, US
$100,000 to $115,000; not specified; university co...
Microsoft fabric development
Scalable data pipelines
Etl/elt design and development
The Lead Analytics Solutions Engineer provides technical leadership for the design, development, and ongoing operation of the University’s cloud-based data lake and warehousing environment, with a primary focus on Microsoft Fabric

Job Summary

  • The Lead Analytics Solutions Engineer provides technical leadership for the design, development, and ongoing operation of the University’s cloud-based data lake and warehousing environment, with a primary focus on Microsoft Fabric.
  • This role is responsible for building scalable, reliable data pipelines and integration solutions that enable enterprise analytics and reporting across academic and administrative units.
  • Partnering closely with data architects, analysts, and campus stakeholders, the role shapes data models and system architecture while also mentoring peers, conducting code reviews, and promoting data engineering best practices.

Matching Summary

The Lead Analytics Solutions Engineer provides technical leadership for the design, development, and ongoing operation of the University’s cloud-based data lake and warehousing environment, with a primary focus on Microsoft Fabric.

Salary

$100,000 to $115,000; Not specified; University contributions to health, dental, life and disability insurance, tuition waivers, 12 holidays, immediate leave accrual, and retirement programs.

Skills & Requirements

Must-have

  • Microsoft Fabric development
  • Scalable data pipelines
  • ETL/ELT design and development
  • Data modeling and integration strategies
  • Data quality and error handling

Nice-to-have

  • Cloud-based data development
  • Collaboration with stakeholders
  • Promoting data engineering best practices

Key Requirements

  • Bachelor's degree
  • Advanced ETL/ELT knowledge
  • Strong code analysis skills
  • Experience with large datasets
  • Data quality and reliability mitigation
  • Change data capture (CDC) concepts
  • Normalization and dimensional design principles

Work Rights

Proof of legal authority to work

Tailored Resume

Cover Letter