Senior Quantexa Developer

Job Summary

The Senior Quantexa Developer will design, develop, and implement advanced decision-intelligence solutions that support financial crime detection across AML, KYC, fraud, sanctions, and related risk areas.

The role combines Quantexa configuration, big data engineering, entity resolution, and graph analytics to build contextual intelligence and connected network views.

The developer will contribute to solution design, data pipelines, risk-typology detection logic, and performance optimization in a cloud-based environment.

Key Responsibilities

Design and implement Quantexa-based financial crime solutions using rules, entity resolution, scoring, and graph analytics.

Develop detection logic aligned with financial crime typologies such as TBML, layering, structuring, mule networks, and sanctions evasion.

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

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

Model entities and relationships to create network-based contextual intelligence views supporting investigations and monitoring.

Optimize data transformations, performance, and graph structures for contextual monitoring and alerting.

Support production operations including incident handling, monitoring, and triage.

Work with CI/CD pipelines using Jenkins, Git, Gradle, or equivalent tools.

Collaborate with cross-functional teams across data engineering, analytics, risk, and platform groups. Required Qualifications

Experience with Quantexa in at least one area: ETL, Batch Resolver (BR), Graph Scripting, Scoring, or Alerting.

Strong data engineering experience with Spark/Scala or PySpark, including data pipelines, data quality, and performance tuning.

Strong cloud experience with AWS or equivalent cloud platforms.

Hands-on experience with CI/CD tools such as Jenkins, Git, Gradle, or similar.

Experience in production operations including incident handling, monitoring, and triage.

Experience implementing data ingestion pipelines for customer, account, transaction, and third-party data sources.

Strong understanding of entity modeling and relationship structures for financial crime detection.

Ability to translate risk detection requirements into scalable technical solutions.

Strong analytical, debugging, and problem-solving skills with attention to detail.

Excellent verbal and written communication skills for collaborating across technology and risk teams. Preferred Qualifications

Experience implementing AML, KYC, sanctions, or fraud detection solutions.

Experience with network/graph-based analytics platforms.

Prior exposure to financial crime lifecycle workflows or investigations.