Data Engineer – Fraud & Risk

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July 9

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Closing Gap

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B2B • Recruitment • HR Tech

Closing Gap is a growth partner and business services firm that helps companies close execution gaps across talent, technology, marketing, and operations. They provide global outsourcing and managed teams, hiring and staffing (including AI-driven screening and hire-train-deploy programs), digital marketing, business automation, development and testing, technology integrations (Zoho, Power Platform), training/upskilling, and business consulting for startups and SMBs. Closing Gap focuses on connecting organizations with top 1% global talent and delivering data‑driven, scalable solutions to boost efficiency, growth, and competitive advantage.

1 - 10 employees

Founded 2024

🤝 B2B

🎯 Recruiter

👥 HR Tech

📋 Description

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

🎯 Requirements

• 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.

🏖️ Benefits

• Flexible work arrangements • Professional development opportunities