AI Data Engineer (3–5 Years Experience) – Banking & Payments

We are looking for a skilled AI Data Engineer to join our team and play a key role in building and maintaining scalable data platforms and AI-ready pipelines within banking and payments systems.

⚙️ Key Responsibilities

• Build and maintain data pipelines (batch, micro-batch, real-time) for banking and payments data
• Develop AI-ready datasets for ML use cases (fraud detection, credit scoring, AML/KYC)
• Implement data ingestion frameworks from multiple sources (core banking, APIs, payment gateways)
• Design and manage data storage solutions (Data Lake / Lakehouse / SQL & NoSQL)
• Ensure data quality, consistency, and validation across workflows
• Support feature engineering and ML pipelines
• Implement streaming solutions (Kafka / Event Hub) for real-time processing
• Optimize performance for handling large-scale transactional datasets
• Ensure compliance with data security and regulations (PCI DSS, GDPR)
• Contribute to CI/CD pipelines and DevOps practices

🛠️ Required Skills

✅ Core:

• Strong experience in Python & SQL
• Hands-on experience with Spark / Databricks

✅ Data Engineering Tools:

• Azure Data Factory (ADF)
• Airflow / DBT / SSIS

✅ Streaming:

• Kafka / Event Hub

✅ Cloud:

• Azure (preferred)
• AWS / GCP

✅ Knowledge:

• Data modeling (OLTP / OLAP)
• AI/ML data preparation pipelines
• Handling structured & unstructured data

🏦 Domain Expertise (Must Have)

• Experience in Banking / Fintech / Financial Services
• Strong understanding of:
• Payment flows
• Transaction processing
• ISO 20022 / SWIFT / Card systems
• Exposure to:
• Fraud Detection
• AML / KYC datasets

🎓 Qualifications

• Bachelor’s in Computer Science / Data Engineering or related field
• 3–5 years of experience in Data Engineering
• Banking experience is highly preferred

⭐ Nice to Have

• Delta Lake / Lakehouse architecture
• Real-time analytics / event-driven systems
• Docker / Kubernetes / DevOps
• Data governance tools