Find & Apply For Data Engineer Jobs in New Kensington, Pennsylvania

This full-time summer internship includes a minimum 10-week assignment and offers a competitive monthly salary. Interns may also be eligible for company-provided housing or a housing stipend based on individual needs. At Arconic, interns are valued contributors who work on meaningful projects, collaborate with leaders across the business, and gain practical experience in a dynamic manufacturing environment. Each intern is paired with both a manager and mentor to support their professional growth throughout the program. As a Data Engineering Intern, you will support data engineering, analytics, and digital transformation initiatives that drive innovation across research, development, and manufacturing operations within the Fabrication Technology team by improving the quality, accessibility, and integration of data used in research, development, and operational projects. You'll gain experience in data engineering, analytics, application testing, process automation, and digital solutions that support data-driven decision making.

Internship Highlights:

Real-world projects with measurable business impact. Exposure to manufacturing operations and industrial technologies. Opportunities to present project results to leadership and participate in the company-wide Intern Presentation Challenge. Networking opportunities with leaders, executives, and fellow interns. Plant tours, volunteer activities, leadership sessions, and social events. Personalized mentorship and professional development.

Basic Qualifications:

Pursuing an undergraduate or graduate degree in Data Science, Data Engineering, Statistics, Computer Science, Computer Engineering, Information Science, or Social Sciences with a strong emphasis in Statistics, Quantitative Analysis, or Data Science Minimum cumulative GPA of 3.0. Expected graduation between August 2027 and May 2028. Experience with and programming in Tidyverse (R) and/or Pandas (Python). Experience cleaning, analyzing, and visualizing data using R or Python. Employees must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is not available for this position. This position requires access to controlled technology, as defined in the Export Administration Regulations (15 C.F.R. §730, et seq.) and/or the International Traffic in Arms Regulations (ITAR). Authorizations from the relevant government agency may be required to meet export control compliance requirements.

Preferred Qualifications:

Familiarity with data organization and tidy data principles. Experience with databases, data pipelines, or data integration projects. Previous internship, research, or work experience in data analysis, engineering, software development, or manufacturing. Participation in extracurricular, leadership, research, community service, or student organization activities. Ability to work independently and collaboratively in a team environment. Demonstrated interest in innovation, technology, and continuous improvement. In this role, you will: Collect, validate, organize, and cleanse data from multiple sources. Improve data collection and entry processes to support automation efforts. Verify data accuracy through audits and quality checks. Support development and testing of R Shiny applications and analytical tools. Conduct UI testing, identify issues, and document findings. Assist with database migration, integration, and data management projects. Support the digitization and organization of records and business data. Help automate reporting, workflows, and business processes. Test and validate Python-based tools and simulation outputs. Collaborate with engineers, data scientists, and technical teams on research and operational projects. Apply analytical and problem-solving skills to real-world business challenges. Present project findings and recommendations to stakeholders and leadership at the conclusion of the internship Gain exposure to advanced analytics, optimization, process modeling, and digital transformation initiatives.