Senior Data Engineer - (Fintech Data)

About this role

We are on the lookout for a Senior Data Engineer (Fintech) to help build the next-generation data foundation for fraud prevention. Be part of building the financial backbone of Delivery Hero. You’ll develop products that empower millions of customers and merchants, from seamless payments to innovative financial solutions like wallets and credit. Your work will support our path to profitability by creating financial flexibility for users and enabling smooth transactions across our markets.

Our Flink-based streaming platform already powers real-time fraud signals. In this role, you will build and enhance historization capabilities, batch pipelines, data quality framework, and observability tooling to make fraud data reliable, traceable, and easy to use for rule evaluation, model training, inference, and backtesting. You will work closely with Data Science and Engineering teams and own the full lifecycle of these data solutions, from design and development to operation and continuous improvement.

Build accurate, point-in-time-correct datasets for model training, backtesting, and analysis. Design and build scalable batch pipelines that complement our existing Flink-based streaming platform. Develop robust controls for data completeness, freshness, anomaly detection, reconciliation, and business-logic accuracy. Build observability capabilities that surface pipeline failures, data drift, and quality issues before they affect downstream models or fraud rules.

Partner with Data Scientists and Fraud Ops to translate fraud-prevention use cases into scalable, resilient data products while maintaining consistency between batch and real-time processing. Cloud Data Engineering: experience building production data pipelines using Python, Airflow, dbt, and BigQuery on GCP or AWS. Stream Processing: hands-on experience with Apache Flink or a similar framework, including event time, state, checkpointing, and late-arriving data.

Data Science Enablement: a strong understanding of feature preparation, model training, backtesting, and inference workflows. Data Quality and Observability: experience building validation frameworks, monitoring, alerting, and data lineage for production systems. Engineering Ownership: a track record of owning data solutions from design and implementation through deployment and operations. Ensuring you and all our Heroes are looked after, happy, and healthy is always on the menu.