AI Data Engineer – Banking & Payments (3-5 yrs)

About the role

We are seeking an AI Data Engineer to design, build and maintain scalable data platforms that support AI‑driven use cases in banking and payments. The role focuses on creating robust pipelines, ensuring data quality, and enabling real‑time analytics for fraud detection, credit scoring and regulatory compliance.

Key responsibilities

• Develop and operate batch, micro‑batch and real‑time data pipelines for core banking, API and payment‑gateway sources.
• Design data lake / lakehouse architectures and manage SQL and NoSQL storage solutions.
• Prepare AI‑ready datasets for machine‑learning models such as fraud detection, AML/KYC and credit scoring.
• Implement streaming solutions using Kafka or Azure Event Hub for real‑time transaction processing.
• Ensure data quality, validation and compliance with PCI DSS and GDPR regulations.
• Collaborate with DevOps teams to integrate pipelines into CI/CD workflows and optimize performance for large transactional volumes.

Required profile

• Bachelor’s degree in Computer Science, Data Engineering or a related field.
• 3–5 years of hands‑on data engineering experience, preferably in banking, fintech or financial services.
• Strong understanding of payment flows, ISO 20022, SWIFT and card systems.
• Experience with fraud detection, AML/KYC data sets is a plus.

Required skills

• Python and SQL programming.
• Apache Spark / Databricks.
• Azure Data Factory, Airflow, DBT, SSIS.
• Kafka and Azure Event Hub.
• Cloud platforms: Azure (preferred), AWS, GCP.
• Data modeling for OLTP and OLAP.
• Delta Lake / Lakehouse concepts.
• Docker, Kubernetes and DevOps practices.
• Data governance tools.