AVP, AI Data Engineer

WHO WE ARE:

As Singapore’s longest established bank, we have been dedicated to enabling individuals and businesses to achieve their aspirations since 1932. How? By taking the time to truly understand people. From there, we provide support, services, solutions, and career paths that meet their individual needs and desires.

Today, we’re on a journey of transformation. Leveraging technology and creativity to become a future‑ready learning organisation. But for all that change, our strategic ambition is consistently clear and bold, which is to be Asia’s leading financial services partner for a sustainable future.

We invite you to build the bank of the future. Innovate the way we deliver financial services. Work in friendly, supportive teams. Build lasting value in your community. Help people grow their assets, business, and investments. Take your learning as far as you can. Or simply enjoy a vibrant, future‑ready career.

Your Opportunity Starts Here.

Why join

Join OCBC’s Data Loss Monitoring (DLM) team to modernise monitoring through AI, automation, data engineering and analytics. You will develop secure, reliable and scalable solutions that strengthen detection, alert review, investigation, oversight and management reporting in a regulated banking environment.

How you succeed

• Deliver AI, automation, data and programming solutions from concept through deployment and production support.
• Translate DLM control requirements and risk scenarios into practical workflows, data products and analytics.
• Build trusted pipelines and AI‑assisted tools that improve triage, investigation, reporting and control effectiveness.
• Apply secure‑by‑design engineering, sound governance and disciplined delivery practices.
• Build DLM domain knowledge and apply it to strengthen monitoring and investigation capabilities.

What you do

AI, Automation & Programming

• Design and deploy AI‑assisted solutions, agents and workflows for DLM alert triage, investigation, case enrichment, escalation and reporting.

• Automate manual DLM processes using Python, APIs, workflow orchestration and scripting.

• Develop prototypes and production‑ready applications for structured and unstructured data, including large or complex files and text.

• Assess AI and automation use cases for accuracy, explainability, human oversight, control impact and operational resilience.

• Simplify DLM processes and reduce manual effort without weakening control quality.

Data Engineering & Analytics

• Design and maintain scalable ETL/ELT pipelines to ingest, transform and deliver DLM monitoring data for analytics, investigations, automation and AI use cases.
• Integrate structured and unstructured sources into trusted datasets, reusable data marts, dashboards and risk analytics.
• Use SQL, Python and data engineering frameworks for cleansing, transformation, reconciliation, quality validation and performance optimisation.
• Implement scheduling, orchestration, monitoring, logging and exception handling for reliable production operations.
• Document data lineage, transformation rules, technical designs and operational procedures.

Data Loss Monitoring & Detection Enablement

• Develop practical knowledge of DLM policies, alert generation, analyst review, investigation, escalation and management reporting.
• Translate DLM control requirements, analyst needs and risk scenarios into sustainable technical capabilities and operating processes.
• Enhance content inspection and detection using regex, pattern matching, Exact Data Matching, fingerprinting, classification and contextual analytics.
• Use data and analytics to identify trends, control gaps, abnormal activity and opportunities to improve risk‑based monitoring.
• DLM transformation initiatives, including AI‑assisted reviews and intelligent automation, risk‑based monitoring and future‑state operating capabilities.
• Deliver DLM transformation initiatives, including AI‑assisted reviews and intelligent automation, risk‑based monitoring and future‑state operating capabilities.

Solution Delivery, Governance & Stakeholder Management

• Convert DLM requirements into technical specifications, solution designs, data models, implementation plans and acceptance criteria.
• Support SIT, UAT, OAT, deployment, production verification, onboarding, warranty support and issue resolution.
• Partner with Technology, Data, Information Security, Compliance, Operational Risk, Internal Audit and Business Units.
• Ensure compliance with technology risk, information security, privacy, data governance, model risk and Responsible AI r