Über
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 at scale. Our 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.
As an Associate Data Engineer, you will:
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
Required technical and professional expertise
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, NumPy, 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 modeling 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
Preferred technical and professional experience
Exposure to distributed data processing tools such as Apache Spark or PySpark
Familiarity with modern data warehouse technologies (e.g., Snowflake, Redshift, BigQuery)
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
IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.
Sprachkenntnisse
- English
Hinweis für Nutzer
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