Backend Development for Fintech
Financial applications demand the highest standards of reliability, security, and data integrity. I build fintech backends that are secure, compliant, and performant.
Why Fintech choose to work with me
- Experience with financial data handling and transaction processing
- Security-first architecture with audit trails
- ACID-compliant database design for financial accuracy
- API design for banking and payment integrations
- Performance optimization for real-time financial data
What Makes Fintech Backends Different
Fintech backends can't afford the "move fast and break things" mentality. A bug in a financial system can mean real money lost. Key requirements include:
- Data accuracy: Financial calculations must be exact. No floating-point approximations. Decimal types and proper rounding are essential.
- Audit trails: Every data change must be logged with who, what, when, and why. Regulatory compliance demands it.
- Idempotency: Payment processing must handle retries safely. Processing a payment twice is unacceptable.
- Security: Encryption at rest and in transit. Token-based authentication. IP whitelisting. Rate limiting.
- Availability: Financial services often need 99.99% uptime with zero data loss.
Key Services
Transaction Processing
Build reliable, ACID-compliant transaction processing systems with proper idempotency, retry logic, and audit trails.
Payment Integrations
Integrate with payment providers (Stripe, Plaid, banking APIs) with proper error handling and reconciliation.
Security Architecture
Implement encryption, authentication, authorization, and audit logging that meets financial industry standards.
Real-time Data Pipelines
Process market data, transactions, and financial events in real-time with low-latency streaming architectures.
Recommended Tech Stack
Technologies I typically use for Fintech
FastAPI with strict Pydantic validation, PostgreSQL with ACID transactions, Redis for rate limiting and caching, event-driven architecture with Kafka or RabbitMQ for transaction processing, and comprehensive logging with structured audit trails.
Frequently Asked Questions
How do you ensure financial data accuracy?
I use PostgreSQL's NUMERIC type (never floating point) for monetary values, implement double-entry bookkeeping patterns, add comprehensive validation with Pydantic, and build reconciliation systems that catch discrepancies automatically.
What security measures do you implement?
Encryption at rest (database-level) and in transit (TLS), JWT-based authentication with short-lived tokens, role-based access control, API rate limiting, IP whitelisting for sensitive endpoints, comprehensive audit logging, and regular security reviews.
Need expert backend development?
I build scalable Python APIs and backend systems. Let's discuss your project.
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