Andrei Martynenko

Financial Crime & Compliance Technology Professional

AML/KYC & Financial Crime Technology | RegTech | Go Backend Development

Financial crime and compliance professional with 8 years of banking experience across AML/KYC, customer due diligence, enhanced due diligence, transaction monitoring, sanctions screening, regulatory reporting and UAT of compliance platforms. Combines first-hand knowledge of regulated banking controls with postgraduate software engineering training and hands-on Go backend development. Particularly suited to AML/KYC systems, financial crime technology, RegTech implementation, systems analysis and technical business analysis roles where auditability, traceability and data integrity are critical.

Projects GitHub LinkedIn Download CV

Profile

SMBC — Moscow, Russia

Deutsche Bank — Moscow, Russia

Alfa-Bank — Moscow, Russia

Modulbank — Moscow, Russia

My background combines eight years of regulated banking compliance experience with postgraduate software engineering training.

I have hands-on experience with:

Customer due diligence (CDD) and enhanced due diligence (EDD)
Risk-based customer review and transaction monitoring
Sanctions screening and escalation of compliance matters
Customer data remediation and compliance reporting
UAT and requirements validation for AML/KYC platforms
Regulatory inspection support and control-framework documentation

Working within regulated banking environments shaped how I approach system design — with a strong focus on correctness, auditability, traceability and data integrity.

This combination is particularly suited to AML/KYC systems, financial crime technology, RegTech implementation and technical business analysis roles.

Engineering Approach

I build backend systems in Go with emphasis on:

Go, REST APIs and gRPC services
PostgreSQL and audit-aware data modelling
Redis caching and idempotent processing
Docker, Git, Linux and GitHub Actions CI
Observability with Prometheus, Grafana and Jaeger
Unit testing and concurrency-safe processing

The focus is building systems where behaviour is explicit, observable, auditable and operationally reliable.

Projects

Financial Crime Compliance Platform

Layered Go platform modelling real AML/KYC controls: explainable risk scoring, maker-checker approval and immutable audit trails.

GitHub

Financial Crime Compliance Platform operations dashboard showing customer counts, open alerts, and a live monitoring-alert queue

Problem / Focus

A layered Go platform (domain / application / infrastructure / transport) modelling real AML/KYC controls, built to demonstrate how financial crime systems should be engineered: explainable, rule-versioned customer risk scoring, a maker-checker approval workflow, and immutable, actor-attributed audit trails.

Key engineering aspects

Idempotent transaction ingestion and deterministic transaction-monitoring rules
Sanctions/PEP/adverse-media screening with reviewer confirmation and false-positive disposition
OIDC Authorization Code + PKCE authentication with role-based access control (analyst/reviewer/admin)
Full observability stack: Prometheus, Grafana, OpenTelemetry/Jaeger distributed tracing
Kubernetes staging deployment, CI with vulnerability scanning and race detection
Disaster-recovery backup/restore drills, documented threat model and service-level objectives

Tech stack

Go PostgreSQL Redis Docker Kubernetes Prometheus Grafana OpenTelemetry GitHub Actions

Proof-of-Signal — Event-Driven Signal Processing Platform

Backend system demonstrating event-driven processing pipelines with deterministic output verification.

GitHub View platform Download presentation

Proof-of-Signal platform architecture screenshot

Problem / Focus

Backend system demonstrating event-driven processing pipelines where a Go API gateway ingests data events and integrates with a Python ML service for signal enrichment. Each generated signal produces a deterministic SHA-256 proof hash allowing outputs to be reproduced and verified across runs. The project focuses on backend architecture patterns rather than trading logic.

Key engineering aspects

Event ingestion and normalization pipelines
Deterministic processing and idempotency
Multi-service architecture (Go gateway + ML service)
Background workers for signal processing
Reproducible outputs using cryptographic hashing

Tech stack

Go PostgreSQL Redis Docker Python (FastAPI)

What it solves

A deterministic event-driven processing pipeline where a Go gateway ingests data events and a Python service enriches them to produce reproducible signals.