Data Analyst (Data Modelling)
ING is hiring a Data Analyst (Data Modelling). Level rates it ; you can apply on ING.
Level's read on this job
- Level rates this data analyst role Little AI ●○○○: aI is not part of the work.
- The posting mentions data modelling, data governance and data analysis among its tools and skills.
- Level classes it as a mid-level role.
- ING has 665 other open roles on Level.
AI in this role
Design, develop, and maintain data foundations and scalable data models supporting Significant Risk Transfer initiatives in Wholesale Banking.
Shape the Future of Wholesale Banking Through Data
At ING, we're empowering people to stay a step ahead in life and business. As we continue our digital transformation journey, we're looking for an experienced Data Analyst to help build the data foundations that support one of the most critical and evolving areas within Wholesale Banking: Significant Risk Transfer (SRT).
If you're passionate about turning complex business requirements into structured, trusted, and scalable data solutions, this could be your next career move.
Role Summary
The Data Analyst - Significant Risk Transfer (SRT) is responsible for designing, developing, and maintaining the data foundation that supports Significant Risk Transfer initiatives within Wholesale Banking. The role serves as a bridge between business and technology, translating complex business requirements into scalable data models while ensuring data quality, lineage, governance, and traceability across multiple lending and risk platforms.
The successful candidate will work closely with stakeholders across Risk, Finance, Capital Management, Data Management, and Technology teams to deliver high-quality data solutions that support regulatory compliance, strategic decision-making, and operational excellence. This role requires strong analytical capabilities, data modelling expertise, and the ability to navigate complex banking data environments.
About the Team
The Significant Risk Transfer (SRT) Team is part of the Data Management & Platform Services (DMPS) area within the Lending Tribe. The team is responsible for establishing and managing the data capabilities that enable Significant Risk Transfer transactions and related risk management initiatives across Wholesale Banking.
Working within an international and multidisciplinary environment, the team collaborates with experts across Lending, Risk, Finance, Capital & Models, Architecture, and Technology to build trusted, scalable, and future-ready data solutions. Through strong data governance, modelling, and analytical capabilities, the team supports strategic business objectives by enhancing risk management practices, optimizing capital usage, and delivering sustainable growth.
As a member of the team, you will contribute to business-critical transformation initiatives, helping shape the future of data-driven decision-making within Wholesale Banking while working in a collaborative environment that values innovation, continuous improvement, and professional development.
What You'll Do
As a Data Analyst, you will take end-to-end ownership of the analytical design and delivery of the SRT data model.
Key responsibilities include:
- Designing conceptual, logical, and physical data models for SRT.
- Defining business rules, data definitions, and ownership structures.
- Translating business requirements into data requirements and source-to-target mappings.
- Analyzing and integrating data across Wholesale Banking lending systems such as LoanIQ, GRID, and Vortex.
- Establishing data lineage, reconciliation processes, and quality controls.
- Performing data analysis and prototyping using SQL and analytical tools.
- Collaborating with cross-functional stakeholders across Risk, Finance, Technology, and Data Management.
- Creating and maintaining documentation, metadata, and decision logs.
- Supporting implementation, testing, and operationalization of data solutions.
What We're Looking For
Required Skills & Experience
- Strong experience in data analysis and data modelling.
- Advanced SQL skills and experience with data mapping, reconciliation, and quality management.
- Knowledge of Wholesale Banking lending data environments.
- Experience working with business and technology stakeholders across multiple functions.
- Strong analytical thinking and problem-solving abilities.
- Excellent stakeholder management and communication skills.
- Ability to operate effectively in evolving and complex environments.
- Significant experience delivering complex data analysis or data modelling initiatives in regulated or business-critical environments.
Nice to Have
- Experience with LoanIQ, GRID, and/or Vortex.
- Exposure to Significant Risk Transfer (SRT) and capital relief topics.
- Experience within financial services, lending, credit risk, capital management, or regulatory reporting environments.
What Success Looks Like
In this role, success means:
- Establishing a trusted and reusable SRT data model.
- Ensuring clear ownership, definitions, and traceability of critical data elements.
- Delivering data solutions that are accurate, explainable, auditable, and scalable.
- Creating alignment across a diverse stakeholder landscape.
- Bringing clarity to complexity while maintaining attention to detail.
- Continuously learning and contributing to the evolution of a growing capability within ING
Why Join ING?
- Make a Real Impact
- undefined
- Work at the Forefront of Innovation
- undefined
- Collaborate Globally
- undefined
- Grow Your Career
- undefined
- Enjoy Flexible Working
How to Apply
Ready to help us improve the future of banking?
Click the apply button on our careers site and submit your application through Workday.
Equal Opportunity
Don't meet 100% of the qualifications? Don't let that hold you back. Studies show some candidates apply only when they meet every requirement. If this role excites you and your experience is close to what we're looking for, we'd love to hear from you.
Job Application Safety Reminder
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- Apply only via official ING platforms. ING uses Workday as its internal recruitment system.
- Check the sender's email carefully. Legitimate communication will always come from@ing.com and/or@myworkday.com.
- No payments or banking details will ever be requested. If someone asks for this information, it's a scam.
About ING
With 65,000 employees and operations in approximately 40 countries, there is no shortage of opportunities for people with initiative who want to help people take a step ahead in life and in business. Do you want to work at the cutting edge of what's possible and at the same time ensure you work with integrity and hold the customer's interests at heart? Do you want to be surrounded by progressive, inspiring, diverse and supportive colleagues? Then there is no better place to invest your talents than at ING. Join us. Apply today.
How we rate this
Data Analyst (Data Modelling) at ING rates 10 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ 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
Pay
- Typical data analyst job
- $112k
This posting doesn't list pay. Use the typical data analyst figure when pay comes up. See data analyst jobs
Questions you could be asked
- Tell me about a project where data modelling was part of your work. What did you do?
- Tell me about a project where data governance was part of your work. What did you do?
- Tell me about a project where data analysis was part of your work. What did you do?
- Tell me about a project where risk management was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Data Modelling, Data Governance, Data Analysis, and Risk Management. 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.
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