Senior Software Engineer (Backend) – Fraud
New
B
BVNKFinancial technology
Location: South Africa, Bulgaria, Poland, SpainFull-TimeSenior
Salary70,000 - 110,000 EUR per year
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Job Details
- Required Skills
- AWSJavaKafkaSpring BootCI/CDMicroservices
Requirements
- Strong, proven experience as a backend engineer building production systems in Java with Spring Boot.
- Hands-on experience with microservices and event-driven architectures, ideally with Kafka or similar messaging platforms.
- Experience building low-latency systems where correctness, idempotency, and auditability matter.
- Comfort working with large volumes of event and transaction data and building pipelines that turn signals into decisions and insight.
- Experience integrating third-party APIs and webhooks, including handling failures, retries, and data quality.
- Experience with cloud platforms, preferably AWS, containerisation, and CI/CD pipelines.
- A solid grasp of secure coding practices and handling sensitive data in regulated environments.
- Ability to take ownership, communicate with technical and non-technical stakeholders, and work in a fast-moving environment.
- Use of AI tools and agentic workflows to speed delivery and improve quality.
- Bonus: experience with fraud detection, transaction monitoring, risk scoring, or account takeover prevention.
- Bonus: experience with rules engines or putting ML models into production for real-time decisioning.
- Bonus: experience with card authorisation flows, 3DS, chargebacks, or payment rails such as Faster Payments, SEPA, or ACH.
- Bonus: experience with stablecoins, crypto, or blockchain-based payments.
Responsibilities
- Design, build, and maintain secure, scalable backend services and APIs for fraud detection, risk decisioning, and controls.
- Build low-latency services that assess risk and approve, decline, or challenge activity in real time.
- Ingest and enrich device, session, behavioural, and transaction data, integrating third-party fraud and risk providers.
- Build risk-based controls such as limits, velocity checks, blocks, and step-up challenges.
- Deliver data pipelines, metrics, and alerting to measure fraud and respond to new attack patterns.
- Work with data and ML colleagues to put machine learning models and AI-assisted workflows into production.
- Collaborate with Fraud Operations, Financial Crime, Cards, Payments, and Data teams to deliver features end to end.
- Contribute to architecture decisions, review code, mentor engineers, and help shape the engineering culture.
- Own services in production, including monitoring, incident response, and continuous reliability and performance improvement.
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