Finance UK FinTech Startup

Real-Time Data Pipeline on AWS + Snowflake for FinTech

Architected and built a real-time data pipeline processing 2M+ events/day for a UK-based fintech startup.

5
Team Members
4 months
Duration
5
Technologies
4
Key Outcomes

The Challenge

The client, a Series B fintech startup providing real-time payment analytics to mid-market merchants, was drowning in data. Their existing batch-processing pipeline (built on cron jobs and PostgreSQL) couldn't keep up with 2M+ daily transaction events. Reports were 24 hours stale, and their compliance team was manually generating regulatory reports — a process that took 3 full-time analysts.

As they scaled from 500 to 5,000 merchants, the system began failing under load, with missed events and data inconsistencies eroding client trust. They needed a modern, scalable data platform — but their 8-person engineering team was fully committed to product development.

Our Approach

We deployed a 5-member data engineering team: 2 AWS/Kafka specialists, 1 Snowflake architect, 1 dbt/analytics engineer, and 1 Power BI developer. The team operated on GMT hours with full overlap.

We designed a streaming-first architecture:

**Ingestion Layer:** Apache Kafka (MSK) for real-time event streaming with exactly-once semantics. Custom Kafka Connect connectors for the client's payment gateway APIs.

**Processing Layer:** AWS Lambda and Kinesis Data Analytics for real-time transformations, fraud flag enrichment, and event deduplication.

**Storage & Modeling:** Snowflake as the central data warehouse with a medallion architecture (bronze/silver/gold layers). dbt for all transformation logic with full version control and automated testing.

**Visualization:** Power BI dashboards with DirectQuery to Snowflake for near-real-time merchant analytics, plus automated compliance report generation.

Project Timeline

1

Architecture & Design

2 weeks

Data source audit, streaming architecture design, Snowflake schema modeling, security review

2

Infrastructure & Ingestion

4 weeks

AWS MSK cluster setup, Kafka Connect connectors, Lambda functions, event schema registry

3

Data Modeling & dbt

4 weeks

Snowflake medallion architecture, 85 dbt models, automated testing, CI/CD pipeline

4

Dashboards & Reporting

3 weeks

Power BI merchant dashboards, automated compliance reports, alert system

5

Migration & Go-Live

3 weeks

Parallel run with legacy system, data validation, zero-downtime cutover, monitoring setup

Key Outcomes

Data latency reduced from 24hrs to 5 minutes
2M+ daily events processed
85% reduction in manual reporting
3x faster compliance reporting

The Results

The new pipeline went live in 16 weeks with zero data loss during the cutover from the legacy system.

Data latency dropped from 24 hours to under 5 minutes. The automated compliance reporting system eliminated the need for 3 full-time analysts (redeployed to higher-value work), saving approximately $180K/year. Power BI dashboards became a key selling point — 3 enterprise clients cited real-time analytics as the deciding factor in signing.

The platform has scaled seamlessly to handle 5M+ daily events as the client onboarded 10x more merchants, with zero architecture changes required.

"We needed senior data engineers who could architect a solution, not just write code. Offshore1st's team designed a pipeline that has scaled 5x without a single architecture change. They're essentially an extension of our engineering team now."
J
James Whitfield
CTO, UK FinTech Startup

Tech Stack Used

AWS Snowflake dbt Kafka Power BI

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