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FinTechData Engineering·16 weeks

Data Platform Modernisation for a FinTech Lender

Three legacy silos consolidated into one real-time intelligence platform

Key outcomes

Reporting latency reduction
50%
Data silos consolidated
3→1
Executive dashboard delivery
Real-time
Regulatory reports on time
100%
The engagement

A fintech lender had data distributed across three disconnected legacy systems — a core banking platform, a CRM and a loan origination system. Reporting required manual exports, took two days and was consistently unreliable. We rebuilt the entire data stack on BigQuery.

IndustryFinTech
PracticeData Engineering
Duration16 weeks
Team4 engineers

Stack

BigQuerydbtFivetranLookerPythonData quality
The challenge

Leadership could not trust their data. Finance, risk and operations teams each had different numbers for the same metric. Month-end reporting was a two-day manual exercise and regulatory reports were frequently late.

Our approach

How we tackled it, step by step.

01

Mapped all data sources, schemas and business logic across the three legacy systems

02

Built event-driven ELT pipelines using Fivetran and custom Python connectors

03

Designed a layered BigQuery warehouse — raw, staging and mart layers using dbt

04

Implemented row-level security in BigQuery aligned to organisational roles

05

Built live executive dashboards in Looker with drill-down to transaction level

06

Delivered a data quality framework with automated alerting on anomalies and missing data

The results

Outcomes that speak for themselves.

50%

Reporting latency reduction

3→1

Data silos consolidated

Real-time

Executive dashboard delivery

100%

Regulatory reports on time

"
We went from waiting two days for reports to having live dashboards. It fundamentally changed how our leadership team makes decisions.
Head of Engineering·FinTech Lender