Data Engineer

OVERSEA-CHINESE BANKING CORPORATION LIMITED; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

OCBC Bank is seeking a hands-on Data Engineer within Group Information Security & Digital Risk Management in Singapore. You will design and deliver secure data pipelines and data products to enable risk insights across the Group, handling both structured and unstructured data in a regulated banking environment.

You will collaborate with risk, business and technology stakeholders to translate requirements into robust, production-grade data solutions, applying best practices in data governance and

Qualifications

• 6+ years hands-on data engineering in large-scale platforms
• Experience with risk management or regulatory analytics is a plus
• Strong SQL, Python, Spark, Hadoop and data modelling skills

Responsibilities

• Build and maintain scalable ETL/ELT pipelines for batch and near-real-time data ingestion
• Transform structured and unstructured data into trusted datasets for risk insights
• Collaborate with risk, business, technology and platform teams across lifecycle stages
• Own deployment, production onboarding and warranty support for data pipelines
• Maintain data lineage and transformation documentation

Skills

SQL

Python

Spark

Hadoop

Kafka

Talend

DBT

Data modelling

ETL pipelines

Productionization

Tools

Teradata

Oracle

Hive

Impala

HDFS

Job description

Why Join

Join OCBC's Group Information Security & Digital Risk Management team and contribute directly to strengthening the Bank's information risk monitoring, analytics and control capabilities. This full-time Data Engineer role is hands-on and delivery-focused: you will build secure, reliable data pipelines and data products that enable actionable risk insights across the Group.

You will work with business, risk and technology stakeholders to transform structured and unstructured data into trusted, production-grade datasets and analytics solutions, while applying sound engineering practices in a regulated banking environment.

How You Succeed

You succeed by being a hands-on data engineering practitioner who can independently translate business and information risk requirements into robust, production-grade data engineering solutions.

Success in this role requires:

• Build and maintain scalable ETL/ELT pipelines for batch and near-real-time data ingestion, transformation and delivery.
• Apply strong SQL, Python, Spark, Hadoop and data modelling skills to solve practical information risk and analytics problems.
• Use orchestration, scheduling and monitoring tools to ensure pipeline reliability, operational resilience and timely issue resolution.
• Collaborate clearly with risk, business, technology, production support and platform teams across requirements, UAT, deployment and warranty support.

What You Do

Data Engineering Delivery

Responsible for developing and enhancing data pipelines and system architecture.

• Create and maintain the optimal data pipeline to enable ingestion from a wide variety of structured and unstructured data sources via Talend.
• Define and understand business requirements to develop and implement solutions.
• Support development and deployment of applications utilising the data pipelines to provide actionable insights.
• Involve in UAT execution with a focus on strategic testing processes and procedures to ensure that business standards and specifications are met.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery and cleansing/transformation.
• Collaborate with multiple stakeholders independently to drive key initiatives and projects.

Solutioning, Testing & Production Support

Translate business and information risk requirements into technical specifications, data models, pipeline design and implementation plans.

• Support SIT, UAT, OAT and production verification, ensuring business standards, acceptance criteria and control expectations are met.
• Own technical delivery through deployment, production onboarding, warranty support and issue resolution.
• Maintain documentation for data lineage, transformation rules, operational procedures and support handover.

Information Security & Digital Risk Management

Support data-driven information risk initiatives, including staff abnormal activity monitoring, control analytics, risk indicators and management dashboards.

• Embed secure-by-design and data governance practices into pipeline development, including access control, data handling, auditability and monitoring considerations.
• Contribute to risk governance, control enhancements and regulatory-aligned reporting through reliable data engineering outputs.
• Help convert information risk use cases into repeatable analytics assets, reusable data marts and sustainable operating processes.

Who You Are

Experience

Minimum 6 years of hands-on experience in data engineering, large-scale distributed data platforms, data warehousing, and analytics engineering.

Experience in risk management and project management will be advantageous.

Technical Skills

• Strong background in traditional structured database environments such as Teradata / Oracle, SQL and PL/SQL.
• Fluent in the management of structured and unstructured data, as well as modern data transformation methodologies and tools such as DBT.
• Proficient in Hadoop ecosystem components, including HIVE, Impala, HDFS, Spark, Scala and HBase.
• Hands-on experience working on real-time data and streaming applications using Kafka or other similar tools.
• Hands-on experience in automation process, including building procedures, ETL and automated job scheduling using data integration tools such as Talend.

Information Risk Knowledge

• Good understanding of technology, information and data risk concepts.
• Awareness of data security controls, including access control, encryption, data handling, auditability and data governance.
• Familiarity with regulatory-aligned technology risk management, information security or operational risk reporting will be advantageous.

Competencies

• Hands-on, analytical and comfortable solving complex data problems independently.
• Strong attention to detail, with a reliability mindset for production-grade delivery.
• Able to communicate technical topics clearly to business, risk and technology stakeholders.
• Collaborative team player who