This is the third blog in our Data Engineering for Banks series, where we explore how EU banks can modernize their data foundations to stay compliant, agile, and competitive.
Catch up on the previous parts here:
→ Why EU Banks Need Stronger Data Engineering
→ Data Engineering Starts with a Data Assessment
In this part, we’ll focus on how to design a robust data architecture that supports compliance, scale, and smart decision-making for European banks.
Across Europe, banks are racing to modernize. But data infrastructure often slows things down. Why?
Because most architectures were never built for the world we live in now:
✔️ Real-time payments and fraud detection demand instant data pipelines.
✔️ Regulations like GDPR, PSD2, and DORA demand full control and visibility.
✔️ Partnerships with FinTechs, APIs, and embedded finance tools mean data moves in and out constantly.
If your bank’s architecture was designed even a few years ago, it’s likely stretched, patched, or overly complex.
In simple terms, it’s the structure that connects how data is collected, stored, processed, and used.
For a European bank today, a robust architecture should:
➜ Be modular – so new components can plug in without affecting the existing systems.
➜ Be governed – so data is traceable, clean, and compliant.
➜ Be real-time-ready – so risk teams, product teams, and analysts all work with current data.
➜ Be interoperable – so legacy systems, cloud services, and APIs talk to each other.
In short, it’s the backbone of any modern banking strategy, from customer onboarding to fraud detection.
Think of your bank’s data architecture like how your branches are set up:
There are entry points, secured vaults, processing rooms, and service counters – all working together. Data architecture follows the same idea.
Here’s how:
This part pulls in information from all the places your bank touches customers or the market.
It includes:
✔️ Account activity from your core banking system
✔️ Customer interactions from your mobile app or branch visits
✔️ Risk, AML, and credit scoring tools
Some of this data arrives in real time (like a credit card swipe). Some in batches (like end-of-day reports). The key is to collect it securely and in a format your teams can use later.
Modern banks often use cloud storage that can expand as needed, but more important is how the data is organized.
We typically see banks creating three main zones:
✔️ Raw zone – untouched data, saved for compliance and audits
✔️ Trusted zone – cleaned and verified information
✔️ Business-ready zone – data that’s packaged and easy to use for dashboards, reports, or AI models
This step involves:
✔️ Cleaning errors
✔️ Standardizing formats
✔️ Matching customer IDs across systems
✔️ Enriching data (like adding customer lifetime value, fraud scores, etc.)
Done right, your teams get data they can trust and use confidently, whether for daily operations or strategic planning.
In EU banking, this part is non-negotiable. Here’s where you:
✔️ Define who can see what
✔️ Track who touched the data and when
✔️ Ensure data stays inside EU borders (GDPR)
✔️ Keep a full audit trail for every field of sensitive data
With new regulations like DORA coming into effect, this layer must be built into your architecture, not added later as a patch.
Finally, your processed and governed data is ready to go to work.
✔️ Product teams use it to design new offerings
✔️ Risk and compliance teams use it to run fraud models or reports
✔️ Business teams get it in dashboards and daily alerts
A strong architecture ensures everyone sees the same source of truth, securely and in real time.
There are four main approaches:
This is the most common and trusted structure in banking. You organize your data systems in clear stages, just like departments in a bank.
→ First, data gets collected.
→ Then it gets stored.
→ Then it’s processed and secured.
→ Finally, it’s shared with those who need it.
Each layer focuses on a specific task. This keeps things clean, trackable, and easy to troubleshoot.
Why it works: Everyone knows where data lives and who’s responsible for what.
In this setup, your data systems follow the same logic as your departments.
For example:
→ Lending has its own data area
→ Cards have their own pipelines
→ Risk and compliance manage their own views
→ Customer support has what they need, separately
This works well for larger banks where each business line moves quickly and needs autonomy.
Why it works: Teams get control of their data, but still follow shared rules.
This is the most common and trusted structure in banking. You organize your data systems in clear stages, just like departments in a bank.
→ First, data gets collected.
→ Then it gets stored.
→ Then it’s processed and secured.
→ Finally, it’s shared with those who need it.
Each layer focuses on a specific task. This keeps things clean, trackable, and easy to troubleshoot.
Why it works: Everyone knows where data lives and who’s responsible for what.
This is a newer idea, mainly suited for very large banks.
Each department or subsidiary owns its own data products, but they all plug into a shared set of rules for security, quality, and compliance.
Why it works: Keeps scale manageable in complex banking ecosystems (when a bank operates across multiple EU countries or divisions)
● If you’re a mid-sized EU bank, a layered or domain-based model usually works best.
● If you’re large and distributed, consider a mesh approach with strong governance.
● If real-time responsiveness matters (like in fraud or lending), event-driven design gives you an edge.
Here’s how strong data architecture shows up in day-to-day banking outcomes:
➜ When data is already organized and accessible, launching a new product (digital loan, ESG-linked investment, or a savings plan for Gen Z) takes weeks, not months.
➜ Real-time data architecture means you can catch issues before they cause damage.
➜ Compliance becomes hassle-free with clear audit trails, column-level data tracking, instant access logs, and proof of data residency for GDPR and DORA.
➜ A modern data architecture lets you expose secure APIs for partners, enforces who can access what, and logs every access request for legal protection.
➜ Gives more confident decision-making across the banking operations.
You don’t need a full audit to spot if your data setup is holding your bank back. Here’s a quick way to assess where you stand today.
Ask these questions:
| Data Collection | Can you connect a new data source (like a fintech partner or new app) in under a few weeks? |
| Data Access | Do your teams get the data they need without waiting on IT every time? |
| Compliance & Audit | Can you track who accessed what and when? Do you have full traceability? |
| Data Quality | Do product or risk teams trust the numbers they see in dashboards? |
| Real-Time Readiness | Can your system flag risky activity while it’s happening, not after the fact? |
| Growth Flexibility | Are you confident your data setup can handle new regulations or expansions? |
If you answered “no” or “maybe” to more than two of these, your architecture might be limiting your growth, compliance posture, or ability to respond fast in a changing EU market.
But the good news?
You don’t need to rebuild everything overnight. You can upgrade in layers, starting with the parts that matter most to your business teams and regulatory priorities.
Most modern EU banks follow a layered architecture that includes built-in governance and auditability. This means sensitive customer data is handled separately, access is logged, and processing is trackable – all aligning with GDPR privacy rules and DORA resilience expectations.
If you’re designing or upgrading, look for storage zones (raw, trusted, curated), column-level access controls, and automated lineage tracking.
Banks in the EU are increasingly moving to lakehouse models, which combine the best of both:
→ Raw storage from data lakes
→ Fast, structured queries from data warehouses
→ Built-in governance and compliance tooling
This lets you store more data (unstructured + structured), run AI/ML models, and still generate audit-ready reports, all in one system.
Say this:
“It’s how all our data flows – where it comes from, where it lives, how it gets cleaned, who can use it, and how we keep it secure and compliant.”
Think of it like the plumbing and wiring of a smart branch. Nobody sees it, but without it, nothing works properly.
Data mesh works best if:
→ You’re a large EU bank with many departments or country-specific operations.
→ You want each business unit to manage its own data pipelines but still follow shared compliance rules.
If you’re mid-sized or centralised, a layered or domain-driven model is likely more practical, for now.
Yes, but only if your current architecture supports event-driven design or can plug in tools like Apache Kafka or Flink.
You don’t need to replace your whole system. You can layer in real-time capabilities starting with specific use cases like card fraud, KYC flags, or payment risk scoring.
1️⃣ Data Pipeline: A series of steps that move data from one system to another, including collecting, cleaning, transforming, and loading it for use by analytics or applications.
2️⃣ Data Lakehouse: A hybrid storage model that combines the flexibility of a data lake (stores raw data) with the performance and structure of a data warehouse.
3️⃣ Domain-Driven Design (DDD): An approach to organizing data and systems based on business functions like lending, payments, or compliance rather than technical departments.
4️⃣ Data Mesh: A decentralized approach where different business teams manage their own data as products, following shared standards.
5️⃣ Metadata: Data about data, such as when it was created, who accessed it, and what system it came from. Vital for tracking, trust, and regulatory reporting.
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