$100,000 to $115,000; not specified; not specified...
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Microsoft fabric expertise
Scalable data pipelines
Etl/elt design and development
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The University of Arkansas is seeking a Lead Analytics Solutions Engineer responsible for developing and maintaining cloud-based data solutions within Microsoft Fabric. The ideal candidate will have a strong background in data engineering, particularly with ETL processes and data quality assurance, and will contribute to the university's mission of supporting student success through effective data management.
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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
Match Score: 75
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The University of Arkansas is seeking a Lead Analytics Solutions Engineer responsible for developing and maintaining cloud-based data solutions within Microsoft Fabric. The ideal candidate will have a strong background in data engineering, particularly with ETL processes and data quality assurance, and will contribute to the university's mission of supporting student success through effective data management.
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Salary
$100,000 to $115,000; Not specified; Not specified
Skills & Requirements
Must-have
Microsoft Fabric expertise
Scalable data pipelines
ETL/ELT design and development
Data modeling and integration strategies
Code reviews and technical leadership
Nice-to-have
Collaborative environment
Service-oriented approach
Promoting best practices
Key Requirements
Bachelor's degree
Strong code analysis, debugging, and performance optimization skills
Experience ingesting, transforming, and managing large datasets
Working knowledge of change data capture (CDC)
Solid understanding of design principles (normalization, dimensional design)