Senior Data Engineer, Anti Financial Crime (Business Analysis Manager)
OKX will be prioritising applicants who have a current right to work in Hong Kong, and do not require OKX's sponsorship of a visa.
Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. What You’ll Be Doing -
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.
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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.
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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.
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Collaborate with modelers & investigators: iterate quickly on feature definitions, and refine logic based on model performance and investigative feedback.
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Maintain a living feature catalogue: version each prototype, capture business meaning, lineage and sample metrics so production data engineers can industrialize it.
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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.
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Stay current: monitor emerging AML data-science techniques (graph ML, LLM embeddings, anomaly detection) and assess their applicability to crypto and fiat monitoring.
What We Look For In You -
4+ years in data engineering / analytics with hands-on feature-engineering and exploratory data analysis; AML or broader compliance experience is a plus.
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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
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Solid grounding in machine-learning fundamentals (supervised, unsupervised, evaluation metrics) and how features impact model performance.
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Experience translating AML / regulatory concepts into quantitative features—e.g., structuring, layering, sanctions exposure.
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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.
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Self-directed, curious, and hungry to experiment with new data sources — blockchain analytics, vendor feeds, public datasets.
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Bonus: Working knowledge of the crypto ecosystem, VASP regulations, and typical AML data flows (KYT, KYC, TM, case management).
Perks & Benefits -
Competitive total compensation package
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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
-
More that we love to tell you along the process!
-
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.
What We Look For In You -
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).
Perks & 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
-
More that we love to tell you along the process!
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).
-
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
-
More that we love to tell you along the process!
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