Intern Data Engineer 2027 – AI & Data Analytics

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, cloud data platforms, AI adoption, generative AI, and agentic AI-enabled data 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 internship, you can build data engineering skills by contributing to client projects that use modern data platforms, cloud services, analytics, machine learning, 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 data pipelines, data products, APIs, retrieval assets, and governed AI-ready data 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 data quality, observability, governance, and responsible AI practices, and you could advance to our full-time Associates program based on results and performance. Work experiences you could be exposed to:  Mentored Data Engineering Support: Receive mentorship from data engineers, architects, AI engineers, consultants, and technical mentors while applying analytical rigor, data modeling, data quality, and responsible AI practices to client challenges. Data Pipeline and Platform Development: Develop skills in writing efficient, reusable code to ingest, transform, validate, model, and serve structured, semi-structured, and unstructured data for analytics, RAG, and agentic AI solution components. Effective Communication: Assisting explaining data flows, data quality, lineage, pipeline health, assumptions, limitations, and recommendations to both technical and non-technical audiences. Tech-Driven Data Builder: Use tools such as Python, SQL, cloud platforms, ETL/ELT tools, APIs, data platforms, and AI-assisted coding tools to turn raw data sources into reliable analytical and AI-ready assets. Currently pursuing a quantitative or technical degree in Computer Science, Data Science, MIS, Business Analytics, Systems Engineering, Industrial Engineering, Mathematics, Engineering, AI/ML, or a related field. Strong interpersonal skills that enhance collaboration and relationship building, while also managing dynamic workloads in an agile environment.  Have initiative and passion to actively seek new knowledge and improve skills while embracing a growth mindset to assimilate diverse viewpoints.  Demonstrate leadership experience and ability to communicate effectively through active listening; while also be willing to adapt and have a readiness to take ownership of tasks and challenges.  Familiarity with programming or query languages such as Python, SQL, Java, Scala, JavaScript, or similar, and interest in data engineering concepts such as pipelines, databases, APIs, ETL/ELT, data modeling, or data quality. Willingness to travel as needed. Exposure to ETL/ELT projects, data warehouse or lake house design, data engineering capstones, data quality checks, lineage, cataloging, or data governance is a plus. Exposure to data engineering technologies such as Spark, Kafka, Airflow, dbt, Databricks, Snowflake, Delta Lake, Linux, APIs, or similar tools is a plus. Exposure to LLMs, embeddings, vector databases, retrieval systems, RAG, prompt design, MCP-based tooling, or agentic AI frameworks such asLangChain,LangGraph, or similar, and AI-assisted Coding tools such as GitHub Copilot, Codex, Claude Code, Cursor, or similar tools for coding, testing, debugging, documentation, and code review is a plus. United States Data & Analytics Internship Chicago, US (0147) International Business Machines Corporation