Do your engineers have experience with PCI-DSS or SOC 2 environments?

Several do. During vetting we ask about their experience with regulated systems, security practices, and how they've handled audit requirements. We flag candidates with direct fintech experience when that's what you need.

Can you source engineers with specific domain knowledge, like payments or lending?

Yes. When you brief us, be specific about the domain. 'Payments experience' and 'lending/credit experience' lead to different candidate pools, and we'll target the right one.

How do you handle IP and data security for fintech clients?

Every engagement includes IP assignment clauses and NDAs. We can also support your security onboarding requirements: background checks, device management policies, and access controls.

What's the typical seniority level for fintech placements?

Most fintech clients ask for mid to senior-level engineers. Junior engineers aren't a great fit for regulated environments where the cost of mistakes is high. We can staff both, but we'll tell you honestly if we think a role needs more experience.

Do you place engineers for short-term compliance projects?

We can. Project-based engagements work well for scoped work like building audit tooling or migrating a legacy payment integration. Staff augmentation makes more sense for ongoing product development.

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.

Build Your Team See Our Vetting

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.

6 use cases

Payment processing and gateway integrations

Stripe, Adyen, Braintree, and custom payment rails. Engineers who've built idempotent transaction flows and handled edge cases like partial captures and refund reconciliation.

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-assisted risk and analytics

Churn prediction, credit scoring models, spend pattern analysis, and anomaly detection: built with your data, deployed in your infrastructure.

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