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Data Engineer, Fraud
Skills
Apache AirflowCross-Team CollaborationData AnalyticsDatabasesFraud DetectionFraud InvestigationsOptimization
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 3+ years of data engineering experience with fraud detection or similar systems
- Proficiency in Python and SQL
- Knowledge of orchestration and transformation tools such as Apache Airflow and DBT
- Strong database design query optimisation and ETL ELT workflow skills
- Experience with relational databases such as MySQL and columnar databases such as ClickHouse
- Experience building and maintaining production data pipelines
- Familiarity with machine learning models and model registries such as MLflow
- Understanding of CI/CD processes for data pipelines
- Experience with data visualisation tools such as Tableau Superset or Metabase
- Ability to support model serving components such as Flask or FastAPI
- Familiarity with statistical techniques for fraud trend analysis and reporting
- Experience with Git and version control in collaborative workflows
Day to day
- Design, develop, and maintain robust data infrastructure for fraud detection and broader data engineering use cases.
- Build scalable, high-performing data pipelines and storage systems that support analytics, reporting, and fraud signal analysis.
- Collaborate with product, data, and engineering teams to operationalise detection models, engineer datasets, and troubleshoot pipeline performance.