Fintech
Engineers who know money moves fast.
Payments, risk, compliance, and core infrastructure: built by developers who've worked in regulated environments and know how much a bug costs here.
What slows fintech teams down
Fintech is hard to hire for.
The domain knowledge gap, compliance requirements, and security expectations mean generic engineers rarely cut it.
Domain knowledge takes months to build
Engineers who haven't worked in financial systems don't intuitively understand settlement flows, reconciliation, or the consequences of a race condition in a payment processor. Ramping them up is slow.
Compliance isn't optional
PCI-DSS, SOC 2, GDPR, and local financial regulations create hard constraints on how you build. Most engineers learn these after they've already shipped the wrong thing.
Security is a first-class concern
Financial systems are high-value targets. Pen testing, threat modeling, and secure-by-default coding aren't nice-to-haves: they're table stakes. Finding engineers with that mindset is genuinely hard.
Scale expectations are unforgiving
Black Friday traffic, end-of-month payroll runs, and real-time fraud checks all happen at the same time. Engineering for those peaks while keeping costs sane requires real experience.
What we build
Specific work, not vague capabilities.
Fraud detection and risk models
Rules-based engines, ML scoring systems, and real-time decisioning layers. Engineers who understand false positive costs as well as miss rates.
Compliance and audit tooling
Automated KYC/AML workflows, audit trail systems, and reporting pipelines for regulators. Built with traceability and immutability as design constraints.
Core banking and ledger systems
Double-entry ledgers, account management systems, and transaction history: engineered for consistency and queryability.
API and third-party integrations
Plaid, Dwolla, Marqeta, banking-as-a-service providers, and open banking APIs. Engineers who handle token management, rate limits, and idempotency properly.
AI in this industry
AI-assisted fraud detection, automated compliance checks, and ML-driven risk models are becoming standard in fintech. Our engineers come prepared for this: several have worked on production fraud scoring systems and credit decisioning pipelines, and understand the data quality and regulatory constraints that make these systems hard to build correctly.
Common tech stack
What fintech teams typically build with.
Node.js
Python
Go
Java
Kotlin
GraphQL
PostgreSQL
Redis
Kafka
AWS
GCP
Terraform
Kubernetes
DatadogFAQ
Common questions
Tell us what you're building.
We'll match you with engineers who've worked in financial systems, not just engineers who are available.