§ 01Snapshot
- Levelsenior individual contributor, around seven years of hands-on backend and full-stack work. Not a CTO; best placed reviewing mid-level and senior PRs, with a senior lead owning the architecture.
- Roles heldsoftware engineer at a consumer-lending fintech; senior software developer at an engineering consultancy (on a US point-of-sale integration account); senior software engineer at product and enterprise companies; earlier engineering at a large telecom.
- Scaleled code reviews for a microservices team on a client account. No people-management scope claimed.
- Industriesfintech / consumer lending · payments and point-of-sale · retail · consumer web platforms · telecom (early career)
- Company typesventure-backed fintech scale-up · mid-size engineering consultancy · large enterprises
- Specialtiesevent-driven payment flows, reconciliation and fraud logic, Spring Boot API design, integration/middleware migrations, auth flows, cleanup of AI-generated code that lacks architectural direction.
- Best forseed and Series A fintech startups · scale-ups with a Java or Node backend that need a second senior reviewer · teams cleaning up AI-assisted code
- Core stackJava, Spring Boot, Hibernate, microservices, Node.js, AWS (SQS), Kafka, MuleSoft (Mule 3 and 4), Vue, React, JWT/OAuth. Trained on the OWASP API Security Top 10.
- Time zonesAsia
§ 02Services
| ✓ | PR/MR code review | Java/Spring, Node, mid-level and senior PRs |
| — | Architecture review | Not offered by this expert |
| — | Architecture design | Not offered by this expert |
| — | Audit / due diligence | Not offered by this expert |
| ✓ | Vibe-code rescue | hands-on cleanup, under a senior lead |
| ✓ | Team mentoring | code-review practice for mid-level engineers |
§ 03Track record
Untangling AI-generated code that delayed a simple process
After joining a new company, one of our engineers found AI-generated code that had turned a simple business process into a slow, costly one. They traced the cause to poor prompting and no architectural direction. They are fixing it by restoring a clear architecture and reworking the generated code to fit it. Outcome is in progress; no numbers claimed.
Event-driven lending backend on SQS and Kafka
A consumer-lending platform had slow request paths. One of our engineers moved parts of the processing to an event-driven design on SQS and Kafka. By their account, latency fell by more than half.
Automated payment reconciliation for a lending app
Matching repayments against expected schedules and provider records took manual effort. One of our engineers automated the payment reconciliation, so mismatches got flagged without someone checking by hand.
Fraud-detection automation in a lending backend
A lending platform needed to catch suspicious activity without slowing approvals for legitimate borrowers. One of our engineers built automated fraud-detection checks into the backend services.
Spring Boot and Hibernate APIs for consumer lending
One of our engineers built and maintained the core lending APIs on Spring Boot and Hibernate. Those APIs served the loan flows end to end.
Node.js backend for a lending mobile app
The lender's mobile app needed its own backend layer. One of our engineers built the Node.js backend behind it, alongside the Java services.
Mule 3 to Mule 4 migration
An integration layer was still on Mule 3. One of our engineers migrated it to Mule 4 and reported a noticeable gain in API performance.
Point-of-sale integration for a fintech product with millions of users
One of our engineers was the technical liaison between a consultancy and a US retail point-of-sale client. They built microservices and a Vue frontend for a fintech product used by millions of people.
Leading code reviews on a Java microservices team
On a client account with several microservices, one of our engineers led the team's code reviews. They set the bar for what got merged.
JWT and OAuth on a consumer web platform
One of our engineers worked on authentication and authorization with JWT and OAuth for a consumer-facing platform.
§ 04What you can book
How we'd review a Kafka or SQS consumer before it touches money
A startup is adding an event consumer that moves or records money. This engineer would review it for idempotency, retry and dead-letter handling, ordering assumptions and reconciliation hooks.
A PR review with concrete fixes and the test cases still missing.
API security pass against the OWASP API Top 10
A team is about to expose a new public or partner API. This engineer is trained on the OWASP API Security Top 10 and has worked on auth in production. They would walk the endpoints for broken object-level authorization, excessive data exposure and weak auth flows.
A prioritized findings list attached to the PRs. A senior lead signs off where findings reach the architecture.
Second senior reviewer for AI-assisted Java PRs
A small team ships quickly with AI assistants, and its PRs are growing without a clear structure. This engineer would review incoming PRs against the intended architecture, flag code that drifts from it, and suggest prompt and task breakdowns that keep generated code in line.
Ongoing PR reviews plus a short guidelines note.