Data Engineer (Fraud & Risk)

Job Title: Data Engineer – Fraud & Financial Crime
Location: Remote (India-based candidates preferred)
Experience: 7+ Years
Employment Type: Full-time
Notice Period: Immediate Joiners Preferred
Domain: BFSI / Fraud Risk / Financial Crime Analytics

About the Role

We are seeking an experienced Data Engineer with a strong background in Fraud Detection, Financial Crime Risk Management, and Data Science. The ideal candidate will have hands-on experience designing real-time data pipelines, building advanced fraud models, and optimizing data flows for analytical and operational use cases.

This role requires a blend of technical expertise and domain knowledge in banking, insurance, and financial services — particularly in managing and mitigating fraudulent transactions and suspicious activity detection.

Key Responsibilities

• Data Pipeline Design & Development: Architect, build, and maintain large-scale real-time data pipelines using tools like Kafka, Spark, or Flink for streaming and batch data.
• Fraud Detection & Risk Modeling: Apply machine learning and statistical modeling to identify anomalies indicative of fraud or financial crime.
• Feature Engineering: Develop robust, scalable features for ML models using structured and unstructured data sources (transactions, logs, behavioral datasets).
• Model Deployment & Integration: Collaborate with Data Scientists to productionize models, monitor performance, and ensure accuracy.
• Tool & Platform Management: Integrate and optimize fraud detection tools such as DataVisor, FICO, Actimize, or IBM Safer Payments.
• Data Governance & Quality: Ensure compliance with data security, AML/KYC regulations, and internal governance standards.
• Collaboration & Communication: Work with product, risk, and engineering teams to translate fraud analytics insights into operational strategies.

Must-Have Skills & Experience

• 7+ years in Data Engineering or Data Science, preferably in BFSI.
• Strong proficiency in Python and SQL for ETL, data processing, and model support.
• Hands-on experience with Kafka, Spark, or Flink for real-time data streaming.
• Deep understanding of Fraud Risk Management and Financial Crime Prevention frameworks.
• Exposure to fraud tools: DataVisor, FICO, Actimize, IBM Safer Payments, or similar.
• Experience in Machine Learning for anomaly detection and predictive modeling.
• Strong data architecture skills across data lakes, warehouses, and ETL orchestration.
• Familiarity with cloud data ecosystems: AWS, GCP, or Azure.
• Experience with statistical analysis, graph models, or unsupervised learning for behavioral insights.
• Excellent analytical, problem-solving, and stakeholder communication skills.

Nice-to-Have Skills

• Experience in fraud risk or compliance functions within financial institutions.
• Knowledge of graph databases (Neo4j, TigerGraph) for entity link analysis.
• Familiarity with NoSQL databases (MongoDB, Cassandra) for unstructured data.
• Exposure to CI/CD, Airflow, or Kubernetes for workflow automation.
• Understanding of data privacy and regulatory frameworks (GDPR, PCI-DSS, AML guidelines).

Educational Qualification

• Bachelor’s or Master’s Degree in Computer Science, Data Engineering, Statistics, or related field.
• Additional certifications in Data Engineering, Fraud Analytics, or Machine Learning preferred.

Key Attributes

• Analytical and business-oriented mindset.
• Ownership-driven with attention to scalability and data quality.
• Ability to communicate technical concepts to non-technical stakeholders.
• Team player with strong remote collaboration skills.

How to Apply

Please share your updated resume to fawas.m@xilligence.com with the subject line:
“Application – Data Engineer (Fraud & Risk) – <Your Name>”

Include in the email body:

• Total Experience
• Relevant BFSI/Fraud Experience
• Current / Expected CTC
• Notice Period
• Key Tools & Tech Expertise

Job Type: Full-time

Work Location: Remote