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 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
We are seeking a Senior Data Engineer to join our Data & Analytics team. You will design, build, and maintain robust on-premise data pipelines, architect flexible lakehouse solutions, and lead data infrastructure initiatives to enable data-driven decision-making across the organization.
This is an exciting opportunity for a talented technical leader to be part of a high-performing engineering team responsible for building scalable, production-grade data platforms that support critical business operations across multiple domains.
Main responsibilities
- Design and develop autonomous, production-grade ETL/ELT data pipelines using Python and PySpark that ingest, transform, and deliver high-quality data while maintaining integrity and performance standards.
- Implement and manage flexible lakehouse architecture across raw, curated, and consumption layers, including data partitioning, cataloging, and metadata management.
- Deploy and manage data pipelines using Kubernetes and Docker to ensure scalability, reliability, and efficient resource utilization in on-premise environments.
- Leverage strong SQL Server expertise to design optimal data models, write complex queries, and perform query optimization across the data platform.
- Establish and maintain robust CI/CD practices for data pipeline deployment, including automated testing, version control, and continuous monitoring.
- Enforce security, governance, and role-based access controls across all data layers while ensuring compliance and auditability.
- Mentor junior engineers, conduct code reviews, and establish best practices across the team.
- Collaborate with Data Scientists, Business Analysts, and stakeholders to deliver datasets aligned with operational and analytical needs.
- Provide L3 support and expert consultation for complex data challenges; evaluate and recommend new tools and practices to improve agility and performance.
Qualifications and Profile
- Bachelor's degree in Computer Science, IT, Engineering, or related field with demonstrated continuous learning ethos.
- 8+ years IT experience; 5+ years hands-on data engineering or data pipeline development
- Expert-level SQL proficiency with strong expertise in SQL Server, including query optimization, indexing, and performance tuning
- Advanced Python programming skills for data processing, automation, and production-grade pipeline development
- Kubernetes expertise – Design, deploy, and manage containerized data pipelines in on-prime environments
- Strong data modeling expertise – Both relational and non-relational concepts
- Proven experience with flexible lakehouse/data lake architecture – Multi-layer data lakes, partitioning strategies, and metadata management, Iceberg tables and optimization
- CI/CD and DevOps practices – Setting up CI/CD pipelines, Git, automated testing, and infrastructure-as-code tools
- ETL/ELT orchestration experience – Apache Airflow or similar tools for scheduling and monitoring batch and real-time jobs
- Hands-on experience with at least one NoSQL database (MongoDB, Cassandra, etc.)
- Hands-on experience with Apache Spark and PySpark for distributed data processing and performance optimization
- Data security and governance – Role-based access control, data masking, and compliance frameworks
- Proven ability to work autonomously on complex projects while maintaining high code quality standards
- Excellent problem-solving, communication, and cross-functional collaboration skills
Preferred qualifications
- Experience with on-premise data virtualization or logical data warehouse concepts
- Understanding of data mesh or data fabric architecture patterns
- Real-time streaming technologies (Kafka, Apache Flink)
- Metadata management and data lineage tools
- Experience mentoring junior engineers or leading technical initiatives
- Agile delivery methodologies and product-oriented data architecture