6+ years in fraud/risk engineering, data engineering for fraud, or technical fraud/risk analytics.
Hands-on experience designing or maintaining fraud/AML rules engines, transaction monitoring systems, or risk scoring pipelines.
Strong SQL skills and familiarity with distributed data systems (Snowflake, BigQuery, Redshift, or similar).
Understanding of event-driven architectures, stream processing, and real-time detection (Kafka, Pub/Sub, Flink, etc.).
Ability to work with engineering teams on API flows, backend logic, and alerting infrastructure.
Deep understanding of fraud typologies (card fraud, account takeover, mule activity, merchant fraud, synthetic IDs).
Good understanding of AML detection logic (structuring, layering, suspicious patterns, velocity signals).
Experience interpreting model outputs and integrating risk signals into systems.
Experience managing technical teams (analysts, engineers, or hybrid profiles).
Ability to convert complex detection problems into clear engineering requirements.
Strong communication skills for working with engineers, data scientists, and risk leadership.

Benefits

Health Insurance
Meal Allowance
25 days of Annual leave (+ Bank holidays)
Public Transportation Card
Frequent team events & activities in the office and outside
Office snacks every day
Friendly, comfortable and informal office environment.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

SQLfraud/AML rules enginestransaction monitoring systemsrisk scoring pipelinesevent-driven architecturesstream processingreal-time detectionAPI flowsbackend logicalerting infrastructure

Soft skills

leadershipteam managementcommunication