Own the Fraud & AML Detection Architecture.
Act as the technical lead for the fraud & AML rules engine, risk scoring logic, and behavioural monitoring pipeline.
Design and improve the event flows, data schemas, triggers, and scoring components used for detection.
Work closely with engineering to implement scalable and low-latency monitoring logic in production.
Build and Optimise Rule Logic.
Lead analysts in designing and refining technical detection rules (syntax, thresholds, conditions, event mapping).
Translate risk appetite into robust, efficient, and data-backed rules.
Create testing frameworks, simulation tools, and regression checks for rules before deployment.
Measure rule performance with clear metrics (latency, false positives, leakage, precision).
Integrate Risk Models & Behavioural Signals.
Partner with data scientists to integrate ML-based risk scores, anomaly detectors, or velocity-based models into the rule engine.
Define how risk signals are weighted, aggregated, or combined with deterministic rules.
Ensure models and rules work coherently within the monitoring architecture.
Engineering Collaboration & Technical Roadmap.
Work closely with platform and backend engineers to improve system reliability and automation: real-time alert pipelines, rule execution framework, data ingestion & streaming, audit logs & version control, monitoring dashboards.
Data & Event Quality Ownership.
Define data requirements for fraud/AML systems: event mapping, attributes, enrichment.
Lead investigations into data discrepancies and improvements to event quality.
Partner with data engineering to ensure the pipeline is fit for detection logic.
Leadership and Team Management.
Manage a small team of technical analysts responsible for rules design and monitoring logic.
Work with the Head of First Line Risk to prioritise work and align on roadmap.
Build a strong engineering mindset in the fraud monitoring team: documentation, testing, performance measurement.

Requirements

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

We trust you, so we offer flexible working hours, as long it suits both you and your team;
Health Insurance;
Physical and mental health support through our partnership with MyFitness;
25 days of Annual leave (+ Bank Holidays);
Possibility to visit other Teya offices to meet colleagues in instances when travel is safe and appropriate;
Friday lunch in the office;
Friendly, comfortable and high-end work equipment and informal office environment;
Hybrid work mode policy.

Applicant Tracking System Keywords

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

Hard Skills & Tools

fraud detection architectureAML rules enginerisk scoring logicbehavioural monitoringSQLevent-driven architecturestream processingreal-time detectiondata ingestionrisk models

Soft Skills

leadershipteam managementcommunicationcollaborationproblem-solvinganalytical thinkingdocumentationperformance measurementprioritizationtechnical guidance