Senior Data Engineer

Adecco; Singapore; On-site; SGD 140,000 - 200,000; Full time

Job summary

Adecco invites applications for a senior data engineering role focusing on building robust data pipelines and lakehouse architectures. You will implement scalable data processing, apply best practices in security and governance, and collaborate with cross-functional teams to drive data-driven decisions.

Ideal candidates have extensive SQL and Python expertise, experience with Airflow, DevOps, and on-premise data platforms, and a strong foundation in data modeling and governance.

Qualifications

• 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-premise 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.)
• Data security and governance - Role-based access control, data masking, and compliance sframeworks
• Proven ability to work autonomously on complex projects while maintaining high code quality standards
• Bachelor's degree in Computer Science, IT, Engineering, or related field with demonstrated continuous learning ethos.

Skills

Data engineering

SQL proficiency

Python

Kubernetes

Data modeling

Lakehouse architecture

CI/CD/DevOps

Airflow

NoSQL

Data security governance

Autonomy in complex projects

Bachelor's degree

Education

Bachelor's degree in Computer Science, IT, Engineering, or related field

Tools

Apache Airflow

Job description

Our client is a leading global financial institution with a strong presence across Asia Pacific, EuropE and other major international markets.

The organization is recognized for its commitment to innovation, digital transformation, and technology excellence, investing significantly in modern data platforms, analytics, cloud-native solution and enterprise engineering capabilities.

Must-have qualifications

• 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-premise 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.)
• Data security and governance - Role-based access control, data masking, and compliance sframeworks
• Proven ability to work autonomously on complex projects while maintaining high code quality standards
• Bachelor's degree in Computer Science, IT, Engineering, or related field with demonstrated continuous learning ethos.

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