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).