Quantexa Developer

We are seeking a skilled Quantexa Developer to design, build, and implement advanced decision-intelligence solutions for detecting and preventing financial crime risks, including AML, KYC, fraud, and sanctions breaches.

This role combines big data engineering, entity resolution, graph analytics, and Quantexa platform configuration to deliver contextual intelligence that enhances risk detection, reduces false positives, and improves investigation efficiency.

Key Responsibilities

Design and implement Quantexa-based AML/KYC/Fraud solutions using entity resolution, rules, scoring, and graph analytics.

Develop detection logic aligned with financial crime typologies (e.g., TBML, layering, structuring, mule networks, sanctions evasion).

Translate AML and fraud risk requirements into technical specifications within the Quantexa platform.

Build Spark-based ingestion pipelines for customer, account, transaction, and external intelligence data.

Model entities and relationships for risk-based network views (customers ? accounts ? transactions ? counterparties).

Optimize data transformations and graph structures to support contextual monitoring and investigations.

Configure and tune entity resolution rules, scoring models, risk indicators, typologies, and alerting logic.

Develop custom Scala/Java components to extend Quantexa functionalities when required.

Integration & Deployment

Deploy Quantexa pipelines in cloud or on-prem environments.

Integrate Quantexa outputs with downstream systems such as case management, alerting, and dashboards.

Support performance tuning, troubleshooting, and production maintenance.

Work with AML investigators, FIU analysts, and compliance SMEs to validate typologies and risk scoring.

Present technical solutions in business terms to compliance and risk stakeholders.

Required Qualifications

Technical Skills

Strong proficiency in Scala or Java with hands-on Apache Spark experience.

Experience with data engineering and Big Data ecosystems (Hadoop, Hive, HDFS, Parquet).

Understanding of entity resolution, network analysis, and graph-based data models.

SQL skills for data validation and quality analysis.

Experience integrating APIs, microservices, and ETL/ELT pipelines.

Familiarity with AML and fraud typologies such as transaction structuring, TBML, sanctions circumvention, watchlist matching, synthetic identities, and mule networks.

Understanding of the AML lifecycle: onboarding/KYC, CDD/EDD, TM alerting, case investigation, SAR reporting.

Tools & Platforms

Experience with Quantexa Decision Intelligence Platform (highly preferred).

Knowledge of cloud platforms (Azure/AWS/GCP) and CI/CD tools (Jenkins, GitLab, Azure DevOps).

Familiarity with Docker/Kubernetes is a plus.

Soft Skills

Ability to translate financial crime risk requirements into technical solutions.

Strong analytical, problem-solving, and debugging skills.

Excellent communication and collaboration engineering, analytics, and compliance teams.

Ability to work in agile delivery environments.

Preferred Qualifications

Knowledge of graph databases (Neo4j, TigerGraph).

Experience with AML transaction monitoring systems (Actimize, SAS AML, Oracle FCCM).

Exposure to ML-based risk scoring or anomaly detection.

Certifications such as CAMS, ICA, or cloud certifications (Azure/AWS).

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