Quantexa Developer

Overview

On Site
Full Time
Part Time
Accepts corp to corp applications
Contract - Independent
Contract - W2

Skills

Software Development
Specification Gathering
Modeling
Network
Management
Dashboard
Performance Tuning
Scala
Java
Apache Spark
Data Engineering
Big Data
Apache Hadoop
Apache Hive
HDFS
Apache Parquet
Fraud
Mule
Onboarding
Know Your Customer
TM
SAR
Reporting
Google Cloud Platform
Google Cloud
Continuous Integration
Continuous Delivery
Jenkins
GitLab
DevOps
Docker
Kubernetes
Soft Skills
Finance
Analytical Skill
Conflict Resolution
Problem Solving
Debugging
Communication
Collaboration
Analytics
Regulatory Compliance
Agile
Graph Databases
Neo4j
Actimize
SAS
AML
Oracle
Machine Learning (ML)
Cloud Computing
Microsoft Azure
Amazon Web Services
Network Analysis
SQL
Data Validation
Data Quality
Microservices
Extract
Transform
Load
ELT

Job Details

  • We are looking for a Quantexa Developer - Financial Crime to design, build, and implement advanced decision-intelligence solutions that help detect and prevent AML, KYC, fraud, sanctions breaches, and other financial crime risks.
    This role combines big data engineering, entity resolution, graph analytics, and Quantexa configuration to create connected views of customers, accounts, transactions, and counterparties.

    You will play a key part in delivering contextual intelligence that improves risk detection, reduces false positives, and enhances investigation efficiency across the financial crime lifecycle.

    Key Responsibilities

    Financial Crime Solution Development

  • 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.

  • Data Engineering & Modeling

  • 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 Quantexa s Contextual Monitoring and investigations.

  • Quantexa Platform Configuration

  • Configure and tune:
    • Entity Resolution (ER) rules
    • Scoring models
    • Risk indicators and typologies
    • Alerting logic for contextual monitoring
  • Develop custom Scala/Java components to extend Quantexa functionalities when needed.

  • Integration & Deployment

  • Deploy Quantexa pipelines into cloud or on-prem environments.
  • Integrate Quantexa output with downstream systems: case management, alerting, dashboards.
  • Support performance tuning, troubleshooting, and production maintenance.

  • Financial Crime SME Collaboration

  • Work with AML investigators, FIU analysts, and compliance SMEs to validate typologies, false positives, and risk scoring.
  • Present technical solutions in business terms to compliance and risk stakeholders.

  • Required Skills & Experience

    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).

  • Financial Crime Domain Knowledge

  • Familiarity with AML and fraud typologies such as:
    • Transaction structuring / layering
    • Trade-based money laundering
    • Sanctions circumvention
    • Watchlist matching
    • Synthetic identities
    • Account takeover / mule networks
  • Understanding of the AML lifecycle: onboarding/KYC, CDD/EDD, TM alerting, case investigation, SAR reporting.

  • Tools & Platforms

  • Experience with the Quantexa Decision Intelligence Platform (highly preferred).
  • Experience with cloud platforms (Azure/AWS/Google Cloud Platform) and CI/CD tools (Jenkins, GitLab, Azure DevOps).
  • Knowledge of 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 across engineering, analytics, and compliance teams.
  • Ability to work in agile delivery environments.

  • Nice-to-Have

  • Knowledge of graph databases (Neo4j, TigerGraph).
  • Prior work with AML transaction monitoring systems (Actimize, SAS AML, Oracle FCCM).
  • Experience with ML-based risk scoring or anomaly detection.
  • Certifications such as CAMS, ICA, or cloud certifications (Azure/AWS).
  • Understanding of entity resolution, network analysis, and graph-based data models.
  • SQL skills for data validation and data quality analysis.
  • Experience integrating APIs, microservices, and ETL/ELT pipelines.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.