Senior Data Engineer in WBAA Discovery

First time published: 13-10-2026 Think Forward! Our goal is to help people advance in life and business. We are a well-established brand with strong financials, global reach, and omni-channel strategies. If you value innovation, agile principles balanced with compliance and quality, reliable service, and a practical work approach, this is the place for you. What department? Within Wholesale Banking Advanced Analytics (WBAA), the Discovery team is a fast-paced group that partners with different areas of ING’s Wholesale Bank to explore, shape and validate AI opportunities. The goal is to define, prioritize, and validate the most impactful AI opportunities for WB, making the most efficient use of our limited resources. During the idea gathering we check on business impact, re-use, scalability and strategic fit. The ideas with the highest potential and priority will move to an AI discovery phase, where they will be evaluated for desirability, viability, and feasibility. We help stakeholders clarify problems, assess data and technical feasibility, and create rapid prototypes that demonstrate business value. Successful initiatives are then prepared for building and enabling ING’s Wholesale Banking teams. Role description As a Data Engineer in WBAA Discovery, you will turn ambiguous business questions into practical, feasible analytics solutions. We are looking for a senior data engineer with a strong interest in AI, particularly LLMs. What you will do Frame (business) problems and clarify requirements with stakeholders. Explore data and assess technical feasibility. Develop rapid proof of concepts and prototypes. Translate findings into clear recommendations and structured handover packages for building teams. How you work Adopt a start-up mindset, using short feedback loops, iterative delivery and pragmatic trade-offs while remaining aligned with banking standards for risk, privacy, security and model governance. Build strong stakeholder relationships and communicate complex ideas clearly. Navigate ambiguity and rapidly changing priorities by structuring problems, making sound decisions with limited information, and adapting as requirements evolve. Collaborate effectively with engineers, architects, analysts, data scientists, UX, and product owners in a fast-paced discovery environment. Do you recognize yourself in this profile: Strong Python programming proficiency Data engineering skills: building scalable data pipelines and optimising data processing (e.g., Spark, Airflow, partitioning, incremental loads, performance tuning), as well as medallion architecture and data quality frameworks (GE, Pydantic) Demonstrated expertise in AI integration, including working with large language models (LLMs) such as Gemini and Claude, implementing retrieval-augmented generation (RAG) patterns, designing and evaluating prompts, and utilizing vector databases. Ability to explore data quickly and assess feasibility (data availability/quality, constraints, and expected business impact). Experience building rapid prototypes/PoCs and translating outcomes into clear recommendations and next steps. Experience designing and deploying cloud solutions, including CI/CD, containerisation (e.g., Docker). Experience with a production-minded development environment (staging, testing, monitoring/observability, reliability). Comfortable with rapidly changing priorities of varying degrees of certainty; you proactively structure problems and drive decisions with limited information. Nice to have (but Not Required): Even if you don't meet every requirement, we encourage you to apply. Experience in these areas is a bonus: ML model training, validation, and experiment tracking experience GCP expertise Familiarity with discovery process or design sprints; strong story-telling and visualisation skills to drive alignment. Experience with Kubernetes and container platforms (e.g., OpenShift). Experience building and maintaining high-load APIs and OpenAPI integration/governance. Basic networking knowledge and familiarity with event-driven architecture. Experience with APIs and service-based architectures (microservices, REST, async programming). Comfortable with working in regulated environments. Familiarity with agentic frameworks (ADK, LangChain, LangGraph, CrewAI) and multi-agent design patterns Basic understanding of Java/Kotlin for connectivity to ING internal systems. Contribution to open-source projects

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