Associate Data Engineer

Join IBM's Associate Program for university hires and help build what's next. From day one, you'll collaborate with global clients and IBM teams on projects spanning digital transformation, cloud strategy, AI adoption, analytics, modern data platforms, and agentic AI-enabled data transformation. As an Associate, you'll work alongside a global cohort of ambitious peers, with access to industry-recognized certifications, digital badges, and at least 40 hours of structured learning per year on IBM's AI-driven learning platform.

This role is ideal for builders who like turning messy data into reliable systems and want to make analytics, generative AI, and AI agents useful, reliable, and safe through governed data, trustworthy context, observable pipelines, and secure interfaces to enterprise systems.

Your Role and 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 affecting analytics or AI workflows.
• Create visually compelling dashboards, reports, and observability views for technical and non-technical stakeholders.
• Ensure data integrity, accuracy, reliability, lineage, and access control through rigorous data governance practices.
• Work with project teams to translate client requirements into operational scenarios, data products, APIs, and solutions.
• Present analytical findings and recommendations clearly, demonstrating the value of data-driven decision-making.
• Collaborate with cross-functional teams on complex business problems using cloud platforms, RAG, vector search, APIs, responsible AI, and agent orchestration patterns.

Consulting and Collaboration Skills

• Analyze business processes and application portfolios to identify where teams need better data, context, tools, automation, or process optimization.
• Use Agile ways of working and project-management practices to deliver production-ready data and AI solutions.
• Communicate with curiosity, clarity, and empathy; ask insightful questions about data meaning, risk, governance, and business impact.

Required Technical and Professional Expertise

• 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.

Preferred Technical and Professional Expertise

• 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.