Data Engineer - AML/KYC

VUI SYSTEMS PTE. LTD.; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

A leading technology firm in Singapore is seeking a data engineer to explore and prototype features using SQL and Python for compliance analytics. The ideal candidate will have over 4 years of experience and expertise in data engineering, particularly in the banking domain and AML practices. Responsibilities include maintaining a feature catalogue and validating data quality. Collaboration in a fast-paced environment is essential, alongside a degree in Computer Science or related field.

Qualifications

• 4+ years in data engineering or analytics with hands-on feature engineering.
• Expertise in SQL and Python within notebook workflows.
• Experience in the banking domain and AML concepts.

Responsibilities

• Explore on-chain, off-chain, fiat, and KYC datasets.
• Prototype features using SQL and Python.
• Validate data quality through QC checks and anomaly detection.
• Collaborate with modelers and refine logic based on feedback.
• Maintain a feature catalogue for production data engineers.
• Support regulatory look-backs and ad-hoc research.

Skills

Data engineering

Exploratory data analysis

SQL

Python

Big Data technologies

Machine learning fundamentals

Collaborative work

Education

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

Master’s degree

Tools

Pandas

PySpark

Spark

Hadoop

Databricks

Alibaba DataWorks

Job description

Responsibilites

• Explore the data landscape: profile on-chain, off-chain, fiat and KYC datasets to understand structures, gaps and lineage.
• Prototype features for ML & rules: translate typologies and investigator hypotheses into measurable candidate variables (e.g., velocity, counterparty risk scores, graph metrics) using SQL/Python and big data.
• Validate data quality & drift: run one-off QC checks, anomaly detection and basic stratified sampling to confirm a feature’s stability before production hand-off.
• Collaborate with modelers & investigators: iterate quickly on feature definitions, and refine logic based on model performance and investigative feedback.
• Maintain a living feature catalogue: version each prototype, capture business meaning, lineage and sample metrics so production data engineers can industrialize it.
• Support regulatory look-backs & ad-hoc research: replay historical data, craft quick queries and surface insights that help Compliance and Compliance Product teams respond to audits or enforcement actions.
• Stay current: monitor emerging AML data-science techniques (graph ML, LLM embeddings, anomaly detection) and assess their applicability to crypto and fiat monitoring.

Requirements

• 4+ years in data engineering / analytics with hands-on feature-engineering and exploratory data analysis; AML or broader compliance experience is a plus.
• Expertise in SQL and Python (Pandas, PySpark, or similar) within notebook workflows, plus hands-on experience with big data stacks such as Spark/Hadoop, Databricks and Alibaba DataWorks
• Working experience in banking domain
• Solid grounding in machine-learning fundamentals (supervised, unsupervised, evaluation metrics) and how features impact model performance.
• Experience translating AML / regulatory concepts into quantitative features—e.g., structuring, layering, sanctions exposure.
• Ability to work collaboratively in a fast-paced, dynamic environment.
• Optional skills: Working knowledge of the crypto ecosystem, VASP regulations, and typical AML data flows (KYT, KYC, TM, case management).

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field; Master’s preferred.
• Minimum 5 years of experience in a similar role.