Brand New Day is building a BIRD-aligned semantic layer that will become the foundation for finance, risk, balance sheet management and regulatory reporting. We are looking for a Senior Data Modelling Analyst who can bring structure to complexity. Someone who enjoys turning ambiguous requirements into clear definitions, consistent data models and implementation-ready mappings.
You will work at the intersection of business and engineering. One day you are aligning finance and risk stakeholders on a single definition. The next, you are helping engineers translate that definition into a scalable and traceable data model.
This role is ideal for someone who enjoys ownership, thrives in situations where not everything is defined yet, and wants to grow towards a hands-on Data Architect position.
What will you do as a Senior Data Modelling Analyst?
As a Senior Data Modelling Analyst, you turn fragmented and ambiguous business input into a coherent, decision-ready data model and engineering-ready mappings. You take ownership of the (BIRD-aligned) logical data model and ensure consistent definitions, clear lineage and a scalable structure across domains.
You do more than just documenting requirements, you shape them. You challenge inconsistencies, make trade-offs explicit and drive decisions needed to build a usable and trusted data foundation. You are comfortable working without complete upfront clarity and are able to shape the path forward. You have the ambition to grow into the role of hands-on Data Architect.
Success in this role means: helping establish a complete, maintainable semantic layer and mapping logic that engineers can implement without reinterpretation, and that stakeholders can trust for regulatory reporting.
Your key activities and responsibilities in short:
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Run focused workshops with Finance, Risk and Balance Sheet Management to turn ambiguous requirements into clear definitions and decisions
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Translate business concepts into structured semantic models (entities, attributes, relationships, grain)
Produce end-to-end mapping logic (source BIRD- reporting), including transformations, lineage and edge cases
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Maintain and evolve the logical data model, ensuring consistency across domains and over time
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Challenge unclear or conflicting requirements and drive resolution towards a single agreed definition
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Collaborate with data engineers to ensure mappings are feasible and implementation-ready
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Document all outputs in a precise, version-controlled and reusable format
What you are expected to deliver
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A complete, BIRD-aligned semantic layer tailored to Brand New Day (entities, definitions, relationships)
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Engineering-ready mapping specifications from source systems to semantic layer and onwards to regulatory outputs
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Clear transformation logic, including business rules, aggregations and handling of edge cases
End-to-end data lineage (source semantic- reporting)
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Consistent data definitions, naming standards and metadata documentation
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Validation and reconciliation logic to ensure correctness and auditability
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Practical guidelines and documentation that enable engineers and future maintainers to extend the model
What does success in the first three months look like?
One priority domain fully defined and mapped (source BIRD
- reporting) with stakeholder agreement
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A working version of the semantic model that is consistent, version-controlled, and actively used by engineers
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Mapping specifications that engineers can implement without reinterpretation
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Key ambiguities in finance and risk definitions resolved and documented
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An established way of working for workshops, decision-making, and documentation
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Trust from stakeholders that the model reflects business reality and supports regulatory requirements
What you’ll receive from us
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25 vacation days + 1 extra day off for celebration!
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The option to work from home two days per week and one week remote per year (two weeks for expats)
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Training budget of €1,000 per year to stay up to date in your field
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€25 per month benefit budget for 1,001 fun or useful things (via Alleo)
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Every week a fully catered lunch at the company’s expense (On other days, you’ll need to arrange your own, but no worries: we’re right next to the Amsterdamse Poort, so you’ll have plenty of options.)
Who are you?
You thrive in environments where precision matters, and actively shape requirements, challenge inconsistencies, and drive toward clear outcomes. You prefer meaningful work, creating models and mappings others can build on and trust. Collaborative, but with strong ownership, you focus on quality, consistency and building data foundations that are practical, scalable, and actually used.
You are a structured, decision-oriented thinker who brings clarity to complex questions. You enjoy working in environments where precision matters. You take ownership of requirements, challenge inconsistencies, and translate ambiguity into clear, practical outcomes. You enjoy building models and mappings that others can rely on. You collaborate easily, but you also take responsibility for the quality and usefulness of what you deliver.
Must-haves:
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Strong hands-on experience in data modelling and data mapping within a regulated banking environment (minimum 6 years)
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The ability to turn ambiguous requirements into precise, testable definitions and mappings
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Proven experience with semantic modelling, data lineage and metadata management
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An ownership mindset to drive decisions and maintain consistency of the logical data model
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Strong communication skills with Finance and Risk stakeholders with the ability to both challenge and align with them
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Sufficient SQL skills to validate data and mappings independently
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Experience working across business and engineering teams, producing implementation-ready specifications
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Familiarity with regulatory data models (e.g. BIRD, FINREP, COREP, AnaCredit)
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Experience with medallion architectures (bronze, silver, gold)
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Experience with Databricks, dbt or similar (modern) data platforms
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Experience with data modelling tools and version-controlled documentation (e.g. Git, Collibra, erwin)
Nice-to-haves:
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Exposure to data architecture concepts and an interest in growing into a data architect role
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Experience in smaller or scaling organisations where structure needs to be built pragmatically
One more thing: research shows that women and members of underrepresented groups often only apply when they meet 100% of the requirements. Does this sound familiar? If so, we encourage you to apply anyway. We look forward to meeting you!
Check out our diversity policy here.
