Data Engineer

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., No file chosen Allowed .docx, .doc, .pdf, .docm files only Portfolio or Additional Documents No file chosen Allowed .docx, .doc, .pdf, .docm files only

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 data quality analysis.
• Experience integrating APIs, microservices, and ETL/ELT pipelines.

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/GCP) 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).