Data Engineer (12 month contract)

PERSOL; Singapore; Sur place; SGD 180 000 - 240 000; Plein temps

Résumé du poste

PERSOL in Singapore is seeking an accomplished Data Engineer to design and implement advanced data science solutions across banking domains. You will lead end-to-end data workflows, automate pipelines, and ensure seamless integration with enterprise databases at scale.

You will collaborate with database engineers, build reusable ML pipelines, and contribute to governance and security practices while delivering high-availability data platforms.

Qualifications

• 10+ years of experience in database, data engineering or relevant technical roles.
• Strong proficiency in SQL and experience with relational databases (e.g., MySQL, Oracle).
• Hands-on experience with ETL tools or frameworks (e.g., Airflow, Informatica, AWS Glue).
• Proficiency in programming languages such as Python and Unix shell scripting.
• Experience with big data technologies (Hadoop, Spark, Kafka) is an advantage.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Strong understanding of data modelling, warehousing concepts, and distributed systems.
• Knowledge of data security, governance, and compliance practices.
• Good communication skills and ability to work with cross-functional teams.
• Strong analytical and problem-solving skills.

Responsabilités

• Design and implement advanced data science solutions across banking domains, with a focus on automation and integration with enterprise database platforms.
• Lead the automation of end-to-end data science workflows, including data extraction, transformation, modelling, and deployment.
• Work closely with database engineering teams to ensure seamless integration between models and large-scale relational and non-relational databases.
• Champion the development of reusable, scalable ML pipelines for model training, evaluation, monitoring, and retraining.
• Contribute to data governance and model risk management processes in line with regulatory standards.
• Develop and optimize data models, data warehousing solutions, and database structures.
• Ensure data quality, consistency, and reliability across all platforms.
• Implement data governance, security, and compliance best practices.
• Monitor and improve pipeline performance, troubleshoot issues, and ensure high availability.
• Automate recurring tasks and improve overall data engineering processes.
• Document data flows, system designs, and technical processes.

Connaissances

SQL

Python

Unix shell scripting

Data modelling

Warehousing concepts

Distributed systems

Communication

Problem solving

Outils

Hadoop

Spark

Kafka

Airflow

Informatica

AWS

Azure

GCP

MySQL

Oracle

Description du poste

Job Description

• Design and implement advanced data science solutions across banking domains, with a focus on automation and integration with enterprise database platforms.
• Lead the automation of end-to-end data science workflows, including data extraction, transformation, modelling, and deployment.
• Work closely with database engineering teams to ensure seamless integration between models and large-scale relational and non-relational databases.
• Champion the development of reusable, scalable ML pipelines for model training, evaluation, monitoring, and retraining.
• Contribute to data governance and model risk management processes in line with regulatory standards.
• Develop and optimize data models, data warehousing solutions, and database structures.
• Ensure data quality, consistency, and reliability across all platforms.
• Implement data governance, security, and compliance best practices.
• Monitor and improve pipeline performance, troubleshoot issues, and ensure high availability.
• Automate recurring tasks and improve overall data engineering processes.
• Document data flows, system designs, and technical processes.

Job Requirements

• 10+ years of experience in database, data engineering or relevant technical roles.
• Strong proficiency in SQL and experience with relational databases (e.g., MySQL, Oracle).
• Hands-on experience with ETL tools or frameworks (e.g., Airflow, Informatica, AWS Glue).
• Proficiency in programming languages such as Python and Unix shell scripting.
• Experience with big data technologies (Hadoop, Spark, Kafka) is an advantage.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Strong understanding of data modelling, warehousing concepts, and distributed systems.
• Knowledge of data security, governance, and compliance practices.
• Good communication skills and ability to work with cross-functional teams.
• Strong analytical and problem-solving skills.