Associate Data Engineer 2027

About the Role

IBM's Associate Program for university hires offers an opportunity to collaborate with global clients and IBM teams on projects related to digital transformation, cloud strategy, AI adoption, process redesign, analytics, modern data platforms, and agentic AI-enabled data transformation. As an Associate Data Engineer, you will help design, build, and improve data platforms, data products, and services that support analytics, machine learning, generative AI, and agentic AI solutions. This role is suited for individuals who enjoy transforming data into reliable systems and contributing to the usability, reliability, and safety of analytics, generative AI, and AI agents.

Key Responsibilities

• Assist in designing and implementing scalable data architecture, data products, and management systems for modern cloud environments, analytics, and AI-enabled use cases.
• Optimize existing data pipelines, retrieval indexes, and data services for improved performance, reliability, data quality, and freshness of AI-ready context.
• Collect, prepare, and analyze structured, semi-structured, and unstructured data to identify trends and provide actionable insights.
• Troubleshoot data-related issues, including data quality challenges, retrieval-quality gaps, processing failures, and inconsistencies.
• Create visually compelling dashboards, reports, and observability views to communicate findings and system insights.
• Ensure data integrity, accuracy, reliability, lineage, and access control through rigorous data cleaning, validation, preprocessing, cataloging, and governance practices.
• Prioritize and translate client requirements into operational scenarios, processes, models, use cases, data products, APIs, tool interfaces, plans, and solutions, collaborating with clients, architects, and AI engineers.
• Present analytical findings, data quality insights, and recommendations to clients.
• Work with cross-functional teams to address business problems, utilizing data expertise across cloud platforms, RAG, vector search, APIs, responsible AI, and agent orchestration patterns, while staying current on modern data stack trends.

Consulting And Collaboration Skills

• Analyze business processes and application portfolios to identify areas for improved data, context, tools, automation, or process optimization.
• Utilize Agile ways of working, planning, and project-management practices.
• Communicate with curiosity, clarity, and empathy, asking insightful questions about data meaning, risk, governance, and business impact.

Requirements & Tech Stack

• High School Diploma/GED.
• Familiarity with one or more programming or query languages such as Python, SQL, Java, Scala, or JavaScript.
• Foundational understanding of data engineering concepts, including data pipelines, databases, APIs, distributed processing, ETL/ELT, data modeling, or data products.
• Basic understanding of cloud computing environments such as AWS, Azure, Google Cloud, IBM Cloud, or similar platforms.
• Ability to apply foundational statistical, machine learning, or information retrieval concepts to data preparation, analysis, search, or model-support work.
• Interest in AI/ML, generative AI, agentic AI, intelligent automation, RAG, LLM-powered systems, or enterprise AI platforms.
• Strong analytical thinking, problem solving, adaptability, teamwork, and communication skills.
• Willingness to travel up to 100%, based on project requirements.

Qualifications

• Bachelor’s degree in a related field such as Computer Science, Data Science, Statistics, Mathematics, MIS, Engineering, AI/ML, or another quantitative field.
• Coursework, projects, internship experience, or portfolio work involving data engineering, software engineering, cloud platforms, analytics, AI/ML, or modern data products.
• Experience with technologies such as Spark, Hadoop, Kafka, Airflow, dbt, Databricks, Snowflake, Delta Lake, Linux, or similar platforms.
• Familiarity with LLMs, embeddings, vector databases such as Pinecone, Weaviate, or pgvector, retrieval systems, prompt workflows, RAG, and AI agent architectures.
• Exposure to orchestration and agentic AI frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, MCP-based tooling, or similar tools.
• Understanding of Git, containers, APIs, testing frameworks, CI/CD, Kubernetes, observability, data governance, privacy, security, or responsible AI practices.
• Preferred certifications, coursework, or credentials such as SnowProCore, Google Associate Cloud Engineer, Google Data Engineer coursework, or related cloud/data credentials.
• Familiarity with AI coding tools and GenAI concepts, including prompt engineering, RAG, fine-tuning, and model evaluation.