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Senior Data Engineer in WBAA Discovery

ING is hiring a Senior Data Engineer in WBAA Discovery. Level rates it ; you can apply on ING.

Level's read on this job

  • On Level's scale, this data engineer role is Works on AI ●●●○: the daily work is on AI products, without building the model.
  • The posting mentions Claude, Gemini and LangChain among its tools and skills.
  • Level classes it as a senior role.
  • ING has 704 other open roles on Level.
  • Posted in the last few days.

AI in this role

claudegeminilangchainlanggraphcrewai
rag

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   



Rewards and benefits
We want to make sure that it’s possible for you to strike the right balance between your career and your private life. Find out more about our employment conditions.


The benefits of working with us at ING include:

  • 25-28 vacation days depending on contract
  • Pension scheme
  • 13th month salary
  • 8% Holiday payment
  • Hybrid working
  • Personal growth and challenging work with endless possibilities
  • An informal working environment with innovative colleagues


About us
Curious about how ING empowers people and businesses to move forward?

Discover what we do and what we can offer you.



Questions?
Please visit our Frequently Asked Questions section to find some answers on questions you might have.


Contact the recruiter attached to the advertisement. Want to apply directly? Please upload your CV and motivation letter by clicking the ‘Apply’ button.

How we rate this

Senior Data Engineer in WBAA Discovery at ING rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGClaudeGeminiLangChainLangGraphCrewAI

Also common in data engineer jobs at this level

Pay

Typical data engineer job
$150k

This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. Walk me through how you've used Claude in your day-to-day work.
  3. What are the limits of Gemini that you've run into, and how did you work around them?
  4. What's a project where you used LangChain hands-on?
  5. Walk me through how you've used LangGraph in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: RAG, Claude, Gemini, LangChain, and LangGraph. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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