Data Engineer

Charterhouse Partnership; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Charterhouse Partnership is assisting a leading financial institution in Singapore to recruit a Senior Data Engineer to design scalable lakehouse platforms, implement governed data products and AI-ready assets.

You will deliver batch and real-time data pipelines, enforce data quality and observability, and partner with AI/data science teams to power NLP, RAG and agent‑driven analytics for banking risk and fraud use‑cases.

Qualifications

• 10+ years hands‑on data‑engineering delivery experience, banking / financial services background preferred

Responsibilities

• Design metadata‑driven ingestion frameworks for structured and unstructured data, covering batch, CDC, streaming, API and file sources
• Develop reusable Spark / Java / Python components; build Bronze‑Silver‑Gold lakehouse layers and implement data modelling, lineage, SLA‑enabled data products
• Tune distributed workloads, implement data quality controls and production observability
• Apply DevOps, CI/CD and IaC on Docker / Kubernetes; operationalise ML models alongside data scientists
• Architect data foundations supporting RAG, agent‑AI and multi‑modal unstructured‑data workflows
• Build internal engineering tooling and align data implementations with financial regulatory requirements
• Troubleshoot production workloads and drive platform reliability improvements

Skills

PySpark

Java

Python

SQL

Data modelling

Lakehouse architecture

ETL/ELT

CDC

NLP

RAG

Cross-team collaboration

Education

Bachelor / Master’s in Computer Science or quantitative‑related discipline

Tools

Docker

Kubernetes

CI/CD

Observability tooling

Iceberg

Kafka

Flink

NiFi

Cloudera Machine Learning

Job description

A leading financial institution is seeking a Senior Data Engineer to build scalable lakehouse platforms, governed data products and AI‑ready data assets in close collaboration with architecture, governance, analytics and business stakeholders.

You will deliver batch & real‑time data pipelines, enforce data quality and observability, and partner with AI/data science teams to power NLP, RAG and agent‑driven analytics for banking risk and fraud use‑cases.

Responsibilities:

• Design metadata‑driven ingestion frameworks for structured and unstructured data, covering batch, CDC, streaming, API and file sources
• Develop reusable Spark / Java / Python components; build Bronze‑Silver‑Gold lakehouse layers and implement data modelling, lineage, SLA‑enabled data products
• Tune distributed workloads, implement data quality controls and production observability
• Apply DevOps, CI/CD and IaC on Docker / Kubernetes; operationalise ML models alongside data scientists
• Architect data foundations supporting RAG, agent‑AI and multi‑modal unstructured‑data workflows
• Build internal engineering tooling and align data implementations with financial regulatory requirements
• Troubleshoot production workloads and drive platform reliability improvements

Requirements:

• 10+ years hands‑on data‑engineering delivery experience, banking / financial services background preferred
• Strong skills in PySpark, Java, Python, advanced SQL; solid knowledge of data modelling, lakehouse architecture, ETL/ELT and CDC
• Practical experience building ingestion pipelines and data products; working familiarity supporting RAG / NLP / agent‑AI workloads
• Hands‑on with Docker, Kubernetes, CI/CD and observability tooling
• Able to translate business & risk requirements into data‑platform designs; good cross‑team communication
• Bachelor / Master’s in Computer Science or quantitative‑related discipline
• Experience with Iceberg, Kafka, Flink, NiFi or Cloudera Machine Learning

EA License no.: 16S8066 Rep no.: R25157345

Only successful applicants will be notified.