Junior Data Engineer

Who we are

Crédit Agricole Corporate and Investment Banking (Crédit Agricole CIB) is the corporate and investment banking arm of Crédit Agricole Group, world’s 10th largest bank by total assets.

Our Singapore center (“ISAP” or “Information Systems Asia Pacific”) is the 2nd largest IT setup (after Paris Head Office) for Crédit Agricole CIB's worldwide business. We work daily with international branches located in 30 markets by:

• Envisioning and preparing the Bank’s futures information systems
• Partnering and supporting core banking flagships and transverse areas in their large scale development projects
• Providing premium In-house Banking applications

This unique positioning empowers us to bring our core banking business a sustainable competitive advantage on the market.

We seek innovative and agile people sharing our mindset to support ambitious and forthcoming technological challenges.

Position

In a challenging and multicultural environment, we are looking for a Data Engineer to join our Digital Excellence Centre (DEC) department of Crédit Agricole CIB. The department handles the development of transversal and international projects.

The ideal candidate is a Python programmer with hands-on experience building data pipelines and integrating data workflows to support ML and analytics use cases. You'll work closely with data engineers, ML engineers, and analysts to design, build, and maintain scalable data pipelines that are robust and production ready.

Main responsibilities

• Design and develop data ingestion, transformation, and delivery pipelines using Python and modern ETL tools.
• Build backend services, APIs or UIs (if ReactJS experience is present) to support ML applications
• Maintain and optimize batch and real-time data pipelines for performance and scalability
• Support the packaging and deployment of machine learning models into production environments.
• Write clean, modular, testable code and participate in code reviews.
• Collaborate with data scientists, engineers and DevOps teams to scalable and reliable data pipeline.
• Support data exploration, feature engineering, and occasional model building where needed.
• Automate data validation, quality checks, and pipeline monitoring.
• Work with cloud platforms and container technologies (Docker, Kubernetes)
• Follow best practices for versioning, logging, and CI/CD.

Qualifications and Profile

• Have Graduate or Master’s degree in the relevant field of AI / ML / Data.
• 1-3 years of experience in software development, ML engineering, or data pipeline engineering.
• Strong programming skills in Python (pandas, scikit-learn, FastAPI or Flask)
• Familiarity with SQL and working with relational databases or cloud data warehouses (e.g., BigQuery, Snowflake, Redshift).
• Knowledge on NoSQL databases (any experience in Graph database is desirable)
• Hands-on with data pipeline tools like Apache Airflow, Luigi, or similar tools.
• Experience with version control (Git), testing, and agile development practices.
• Experience with CI/CD pipelines and containerization (Docker, Kubernetes).
• Exposure to cloud environments (AWS, GCP, or Azure) and containerization (Docker).
• Strong debugging and problem-solving skills.