Job Specifications
Introduction
About The Client Innovation Center (CIC)
IBM Consulting Client Innovation Centers (CICs) are high-delivery, team-based environments where technologists work onsite to build real solutions for real clients.
At CIC, interns collaborate closely with data engineers, data scientists, and consultants to support data platforms, pipelines, and analytics solutions across industries. Our delivery centers are built for learning through delivery, combining hands-on project exposure, structured training, mentorship, and teamwork to help students develop strong data engineering foundations and prepare for early-career technical roles.
This role is ideal for individuals who enjoy working with data, building systems, and learning how large-scale data solutions are delivered in an in-person, collaborative environment.
Your Role And Responsibilities
The Data Engineer Intern role is a developmental, learning-focused position that provides hands-on exposure to data engineering concepts, tools, and delivery practices in a professional consulting environment.
This role is not about owning production pipelines or designing enterprise data architectures on day one. It is about applying academic knowledge, learning how data systems are built and operated, and supporting delivery teams with data ingestion, transformation, and quality activities.
High-performing interns may be considered for conversion into IBM Consulting’s full-time Associate Data Engineer program based on performance and business needs.
As a Data Engineer Intern, You Will
Support data ingestion, transformation, and preparation activities under the guidance of experienced data engineers
Assist with building and maintaining ETL/ELT pipelines using established tools and frameworks
Help validate data quality, accuracy, and completeness across datasets
Contribute to data processing tasks using SQL and programming languages such as Python
Gain exposure to modern data platforms, including cloud-based data lakes and warehouses
Participate in Agile or project-based delivery activities such as stand-ups and sprint reviews
Help document data flows, pipeline logic, and implementation details
Build technical and professional skills through mentorship, training, and hands-on project work
Required Technical And Professional Expertise
These qualifications are essential for success in the role.
Technical Foundation Skills
Coursework or hands-on experience in Computer Science, Data Science, Engineering, Information Systems, Mathematics, Statistics, or a related technical field
Basic programming experience in Python or another programming language gained through coursework, labs, or projects
Foundational understanding of SQL and relational data concepts
Familiarity with core data concepts such as tables, schemas, transformations, and data validation
Analytical And Learning Skills
Strong analytical and problem-solving skills, with the ability to approach tasks using structured, logical thinking
Comfortable working onsite in a collaborative, team-based environment
Ability to work effectively in a technology-driven consulting environment where tools, platforms, and client needs evolve over time
Willingness to learn new tools, platforms, and data technologies in a delivery setting
Ability to learn new tools, techniques, and technologies in a delivery setting
Ability to follow established processes and seek guidance when needed
Education
High School Diploma or GED
Preferred Technical And Professional Experience
Preferred Skills (Nice-to-Haves):
Exposure to ETL/ELT concepts or tools through coursework or projects
Familiarity with modern data platforms or tools (e.g., dbt, Snowflake, BigQuery, Spark, Airflow, or similar)
Exposure to cloud platforms (AWS, Azure, Google Cloud, or IBM Cloud) through labs or academic projects
Experience working with datasets for analytics or machine learning coursework
Participation in team-based academic projects, hackathons, internships, or capstone courses
Preferred
Currently pursuing a Bachelor’s degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, AI, or a related field.