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Data Engineer - Anti Financial Crime (Business Analysis Manager)

OKX
Singapore
Full time
On site

Overview

Department

Data Science & Analytics

Job type

Full time

Compensation

Salary not specified

Location

Singapore, East Asia

Company size

Mature [ 50+ employess ]

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This Data Engineer role focuses on profiling datasets, prototyping features for ML and rules, validating data quality, and collaborating with modelers and investigators. The role also involves maintaining a feature catalogue and supporting regulatory research.

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
  • 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.
  • Strong exploratory mindset: you’re comfortable with messy, ambiguous data and love turning it into structured insight.
  • Effective communicator who can collaborate with downstream data engineers and data scientists and explain feature logic to investigators and auditors.
  • Ability to work collaboratively in a fast-paced, dynamic environment.
  • Self-directed, curious, and hungry to experiment with new data sources — blockchain analytics, vendor feeds, public datasets.
  • Bonus: Working knowledge of the crypto ecosystem, VASP regulations, and typical AML data flows (KYT, KYC, TM, case management).

    • Responsibilities

  • Explore the data landscape: profile on-chain, off-chain, fiat and KYC datasets to understand structures, gaps and lineage. Document findings for downstream engineering teams.
  • 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.

    • Benefits

  • Competitive total compensation package
  • L&D programs and Education subsidy for employees' growth and development
  • Various team building programs and company events
  • Wellness and meal allowances
  • Comprehensive healthcare schemes for employees and dependants
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