Mastercard

Context

The challenge

What it does

• Multitenant API gateway with rate limiting
• Near real-time financial event ingestion
• Fraud detection and risk scoring
• Per-tenant analytics on OLAP
• Internal transaction monitoring dashboards

What I did

• Designed internal APIs and reusable backend components: Python API gateways and middleware for routing, rate limiting and tenant-scoped authentication.
• Built Python consumers and producers over Kafka and gRPC, with Protobuf schema validation, for the low-latency event ingestion system.
• Integrated MCP-based AI models for fraud detection and risk scoring through secure API wrappers.
• Designed schema strategies for ClickHouse OLAP tables, optimizing cross-tenant analytics workloads.
• Built and maintained ingestion and transformation pipelines with Apache Airflow, authoring the DAGs for ETL flows and reporting layers.
• Developed internal React tools for transaction monitoring: visualizing payment flows, spotting anomalies and drilling into per-tenant metrics.

Stack

Python

API gatewayMiddlewareKafka consumersApache Airflow

Messaging

Apache KafkagRPCProtobuf

Data

ClickHousePostgreSQLETL

JavaScript

ReactDashboards

AI

MCPRisk scoring

Multitenant

Rate limitingTenant authData isolation

Services practised here

• Platforms & Distributed SystemsWhen a product grows, the problem stops being a feature and becomes how the services talk to each other. That is where service boundaries, event delivery and flows that cannot be lost along the way come in.
• AI Integration & AutomationModels and agents go into the product you already have with the same discipline as any other service: a contract of your own, cost limits and a log of what they did.

Outcome