Co-Op Data Engineer - Data Services 2027

Launch your career like an IBMer Every IBMer has a story. For many, it started with an internship. As an IBM intern, you won't just gain experience - you'll start thinking, working, and growing like an IBMer. From day one, you'll contribute to real client projects across diverse industries, including analytics, AI adoption, generative AI, and agentic AI-enabled transformation, working alongside experienced IBMers who are invested in your success. You'll be challenged, supported, and inspired, often all in the same day. You'll develop technical expertise and consulting skills in a culture built on continuous learning, mentorship, and coaching. High-performing interns have a clear pathway into IBM's Associate Program, launching careers at one of the world's most innovative technology and consulting companies. To give yourself the best opportunity for success, we advise applying only to roles that align with your skills and experience, rather than applying broadly across all entry-level positions. You'll receive a status update email for each application, so be sure to check your IBM Careers account regularly — it's the best way to get a centralized view of which roles you have active applications against. During your Co-Op, you can build data science and AI skills by contributing to client projects that use statistics, machine learning, data science, GenAI, and agentic AI solution patterns. This role provides an opportunity to build a compelling portfolio, acquire new skills, gain insight into diverse industries, and contribute to client-ready analytical and AI solution components with guidance from IBM teams. At IBM, we prioritize continuous learning, skill development, and personal growth within a culture of coaching and mentorship. As an intern, you'll strengthen technical, analytical, and consulting skills while learning responsible AI practices, and you could advance to our full-time Associates program based on results and performance. Support strategic consulting initiatives focused on Enterprise Application Integration (EAI). Assist in analyzing business processes, integration challenges, and technology strategies while working with consultants and stakeholders to identify opportunities for improving business outcomes through connected systems and digital transformation. Key Responsibilities: Conduct research on integration trends, APIs, middleware, cloud platforms, and emerging technologies. Support assessments of business processes and application landscapes. Assist in developing strategy recommendations, presentations, and client deliverables. Analyze data and identify integration opportunities across enterprise systems. Participate in workshops, stakeholder interviews, and requirements gathering sessions. Contribute to business cases, roadmaps, and transformation strategies. Support project planning and change management activities. Co-op Schedule: Year-long co-op at Penn State University Park, starting May 2027; up to 20 hrs/week during classes and 40 hrs/week during academic breaks. Pursuing a degree in Business, Information Systems, Computer Science, Engineering, or related field. Strong analytical and problem-solving skills. Interest in digital transformation, enterprise architecture, and integration technologies. Excellent communication and presentation abilities. Familiarity with cloud, APIs, data integration, or enterprise applications is preferred. Core Competencies: Strategic Thinking, Business Analysis, Collaboration, Research, Communication, Adaptability Ability to work up to 20 hours per week when classes are in session (fall/spring semesters) and up to 40 hours per week during academic breaks (e.g., summer). Can understand clients' needs and challenges, applying a creative and logical approach to identify solutions. Demonstrate foundational quantitative analysis skills and ability to develop data driven recommendations. Exposure to one of the following programming technologies (e.g., JavaScript, HTML, XML, SQL) is a plus. Exposure to Cloud native projects (academic, personal, or internship count); Agile ways of working (ambiguous requirements, evolving scope); Programming (read, debug, and explain code). Exposure to Microservices architect and cloud platforms (Azure, AWS). Exposure to cloud and AI concepts such as Azure services, data platforms, security, and AI models through coursework, labs, internships, or personal projects, with a willingness to keep learning. Exposure to tools like Copilot, ChatGPT, or similar AI assistants to research, write, analyze, and improve productivity, while applying critical thinking and validation.