Associate Data Engineer – Client Innovation Center; Entry Level

Overview

Introduction 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, associates collaborate closely with peers and experienced practitioners to design, build, test, and support enterprise applications  delivery centers are built for learning through delivery, combining hands-on project work, structured training, mentorship, and teamwork to help early-career professionals develop strong technical foundations and grow with confidence.

This role is ideal for individuals who enjoy problem-solving, learning quickly, and working in an in-person, collaborative delivery environment.

Your Role And Responsibilities The Associate Data Engineer role is entry-level and focuses on supporting the development, operation, and improvement of data pipelines and platforms within a broader delivery team. This role is not about owning data platforms on day one. It is about applying strong programming and data fundamentals, learning how enterprise data systems are built and operated, and contributing to data engineering work under the guidance of experienced practitioners.

Associates are expected to contribute to established delivery teams and progressively assume greater responsibility and ownership as their skills and experience develop.

• Support the development and maintenance of data pipelines used for analytics, reporting, and machine learning
• Assist with extracting, transforming, and loading (ETL/ELT) data from multiple sources into data platforms
• Contribute to data cleansing, validation, and transformation activities using Python and SQL
• Help prepare datasets for downstream consumption by analytics and data science teams
• Support batch and, where applicable, near-real-time data processing workflows under guidance
• Collaborate with data engineers, data scientists, and other team members in Agile delivery environments
• Build data engineering skills through training, mentorship, and hands-on delivery experience
• Work with functional and technical team members to help integrate data solutions into client business environments

Preferred Education
Master's Degree

These qualifications are essential for success in the role.

Core Foundations

• Strong foundation in computer science fundamentals, including data structures and algorithms
• Strong analytical and problem-solving skills with attention to data quality and reliability
• 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
• Strong analytical and problem-solving skills, with the ability to approach complex tasks using structured, logical thinking
• Ability to learn new systems and technologies quickly and apply them in a delivery setting

Programming & Data Skills

• Proficiency in Python (preferred) or another programming language used for data processing
• Hands-on experience using data manipulation tools such as pandas, Num Py, and SQL, gained through coursework, labs, projects, or internships
• Ability to write clear, maintainable code for data transformation and processing tasks

Data Engineering Fundamentals

• Understanding of ETL/ELT concepts and how data moves from source systems to consumption layers
• Familiarity with relational databases and SQL for querying and data manipulation
• Basic understanding of data modelling concepts such as schemas, normalization, or dimensional models

Platform & Cloud Awareness

• Exposure to cloud-based data or analytics platforms (e.g., AWS, Azure, or Google Cloud) through coursework, labs, or projects
• Familiarity with core cloud data services such as object storage, databases, or analytics services

Business & Delivery Skills

• Ability to translate business or functional requirements into technical solutions, with guidance from senior team members
• Comfortable working onsite in a collaborative, team-based environment
• Strong willingness to learn, accept feedback, and continuously improve

Emerging Technology Awareness

• Familiarity with generative AI concepts, including basic modeling approaches, responsible use, and ethical considerations, gained through coursework, projects, or self-study

• Exposure to distributed data processing tools such as Apache Spark or Py Spark
• Familiarity with modern data warehouse technologies (e.g., Snowflake, Redshift, Big Query)
• Exposure to streaming or event-based data concepts
• Familiarity with version control tools such as Git
• Basic awareness of how data engineering supports machine learning workflows