A fast-growing neo bank needed to build its data infrastructure from scratch — one that could scale from thousands to millions of customers while meeting the exacting data governance and regulatory requirements of the financial services sector. We designed and built their entire Google Cloud data estate, from raw data ingestion to regulatory reporting and customer analytics.
The Challenge
Neo banks grow fast — faster than most data teams can keep up with. This client had outgrown their initial BI tool and was facing a data quality crisis: inconsistent customer records, unreliable transaction counts, and an inability to produce accurate regulatory reports. They needed to rebuild their data estate properly, without disrupting the business or compromising existing reporting.
Our Solution
We architected a layered data platform on Google Cloud: Cloud Pub/Sub for event streaming, Dataflow for stream and batch processing, and BigQuery as the central analytical warehouse. A medallion architecture (bronze, silver, gold) enforces data quality at each layer. dbt manages all transformations with full lineage and testing. Looker Studio and a custom analytics API serve internal teams and regulatory reporting workflows respectively.
Medallion Architecture on BigQuery
The bronze layer captures raw events exactly as emitted by source systems — immutable and fully auditable. Silver applies deduplication, standardisation, and enrichment. Gold layer tables are business-ready, fully tested, and power all downstream consumption. This architecture makes it straightforward to trace any number back to its source event.
Regulatory Compliance by Design
Data lineage, retention policies, and access controls were designed into the platform from day one — not bolted on after the fact. Every data asset has a documented owner, classification, and retention period. Automated deletion jobs enforce data minimisation requirements. The compliance team has a live view of the data estate at all times.
Customer Analytics Foundation
A customer 360 model in the gold layer stitches together account, transaction, and product data into a single customer entity — enabling the product and growth teams to analyse behaviour, measure feature adoption, and identify at-risk customers with confidence in the underlying data.
Technology Stack
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