Quality Data Engineering Intern

Shure•Niles, IL

23h•$23 - $43•Hybrid

About The Position

The Quality Data Engineering intern will handle a variety of tasks and projects in the Quality Data Engineering team under supervision of the CQE Niles Associate Staff Managing Engineer. This Internship is Onsite or Hybrid based in Niles, IL. Shure offers a challenging, fun, and rewarding summer internship program. The twelve-week program is offered to undergraduate and graduate students. We offer internships with a variety of work arrangements from onsite interns to fully remote in US. Each intern will receive a competitive salary. Additionally, Interns who are asked to relocate to Illinois for onsite internships will receive a housing stipend to cover living expenses. Applications will be collected, reviewed, and selected candidates will be contacted in late fall/early winter.

Requirements

Pursuing bachelor’s degree in Engineering, Computer Science, Information Technology or closely related field, with a minimum of two years of engineering coursework.
Intermediate working knowledge of MS Office applications Word, PowerPoint. Advanced to expert working knowledge of MS Excel.
Intermediate to advance knowledge of one or more programming languages, such as Python, Java, C/C++, SQL, etc.
Basic familiarity with generative AI tools and strong interest in AI-enabled business solutions. Copilot Studio experience is a plus, not required.
Logical problem-solving mindset, attention to detail, curiosity, and willingness to learn.
Basic to Intermediate knowledge of data visualization tools such as Tableau.
Basic to Intermediate knowledge of Microsoft Power Platform tools (Power Automate, PowerApps) and Microsoft Cloud and Collaboration Services (SharePoint Online, Teams, OneDrive)
Basic knowledge of database management, structure, and data governance applications.
Basic Knowledge of Statistics and common terms used.
Clear written and verbal communication; able to work collaboratively in a global, cross-functional environment.
Applicants for this position must be currently authorized to work in the United States on a full-time basis. Shure will not sponsor applicants for this position for work visas.

Nice To Haves

Copilot Studio experience is a plus, not required.

Responsibilities

Conducting quality data analytics and assisting in the development of data visualization reports
Engage with Quality associates to understand processes, data sources, reporting needs, and opportunities for improvement.
Assist with collecting, cleaning, validating, analyzing, and visualizing quality data using tools such as Tableau, SQL, Python, and Microsoft Power Platform.
Help develop and test Power Apps, Power Automate and Power Pages solutions that reduce manual work and improve data visualization and consistency.
Support quality dashboards, metrics, data documentation, user guides, and service requests.
Participate in root cause analysis and data reviews to identify trends, issues, and improvement opportunities.
Support the other members of the Quality Data Engineering team in conducting data quality monitoring protocols and implementing data cleanup activities as needed.
Assist in the resolution of Quality Engineering service requests from the Quality team.
Apply foundational knowledge of generative AI, prompt design, data grounding, model limitations, privacy, and responsible use.
Develop an introductory understanding of agentic AI concepts, including goals, instructions, tools/actions, knowledge sources, orchestration, human oversight, and evaluation.
Build and test basic copilots or agents in Microsoft Copilot Studio, including topics/instructions, knowledge connections, simple actions or flows, testing, and documentation.
Explore practical AI use cases for Quality, such as knowledge retrieval, request triage, guided analysis, or workflow support.
Attend weekly data engineering staff meetings.
Collaborate with Quality, IT, and other business teams while following data governance, security, and responsible AI practices.
Present project findings, lessons learned, and recommendations to stakeholders.