Data Engineer, Fraud
Core
Designing and maintaining robust data infrastructure and pipelines to support fraud detection, analytics, and reporting.
Role type
Data Engineer (Fraud Detection)
Builds
Scalable data pipelines, storage systems, and engineered datasets for fraud use cases.
Domain
Financial services / Fraud detection
Deliverable
production ML models | product features
Required skills
Python, SQL, data pipeline orchestration, ETL/ELT workflows, database design, query optimization, CI/CD for data pipelines, model serving frameworks, data visualization tools, statistical analysis for fraud trends
Preferred skills
Experience with fraud detection systems, knowledge of relational and columnar databases, familiarity with machine learning model registries
Technologies
Apache Airflow, DBT, MySQL, ClickHouse, MLflow, Tableau, Superset, Metabase, Flask, FastAPI
Responsibilities
Build and maintain efficient, scalable data pipelines for fraud detection; enable analysts to identify emerging fraud patterns through engineered datasets; integrate external fraud detection models into data infrastructure; design and optimize data storage solutions for fraud signals; create fraud-specific datasets and features; monitor fraud data pipelines for reliability and performance; document best practices for fraud-related data engineering; partner with cross-functional teams to address fraud trends
Seniority
Mid-level, hands-on IC
