Senior Engineering Manager - Fraud & Transaction Monitoring

Hello! We're Teya.

Teya is a payment and software service provider, headquartered in London serving small, local businesses across Europe. Founded in 2019, we build easy to use, integrated tools that enable our members to accept payments and boost business performance.

At Teya we believe small, local businesses are the lifeblood of our communities.

We’re here because we don’t believe there’s a level playing field that gives small businesses with a fighting chance against the giants of the high street.

We’re here because we see banks and legacy service providers making things harder for them. We don’t think the best technology or the best service should be reserved for those with the biggest headquarters.

We’re here to fight for a future where small, local businesses can thrive, and to commit the same dedication they offer all of us.

Become a part of our story.

We’re looking for exceptional talent to join our mission. We offer a chance to create impact in a high-energy and connected culture, while benefiting from continuous learning opportunities, a supportive community which is proud to serve our mission, and comprehensive benefits.

Your Mission

We are looking for a highly technical Senior Fraud & Transaction Monitoring Engineering Manager to lead the analytical backbone of our fraud and AML monitoring capabilities. This role goes beyond operations: it focuses on building, optimising, and scaling the detection systems, rule engines, behavioural signals, and data flows that protect the Teya ecosystem.

You will work closely with Data Engineering, Risk Analytics, Product, Data Science, and Platform Engineering to design the infrastructure and logic behind rule-based and model-driven monitoring.

With a Head of First Line Risk already acting as PM, your role will be the technical owner of how monitoring systems work end-to-end.

Key Responsibilities

• 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.

• 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).

• Partner with data scientists to integrate ML-based risk scores, anomaly detectors, or velocity-based models into the rule engine.

• Define how risk signals be weighted, aggregated, or combined with deterministic rules.

• Ensure models and rules work coherently within the monitoring architecture.

• 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

• 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.

• 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.

Your Story

• 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, Big Query,…