Remote Senior Data Analyst Compliance & Detection

Wwshemi; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Kraken is seeking a Sr. Data Analyst, Compliance to join our team remotely. You will own end-to-end data management and the logic behind transaction monitoring across deposits and withdrawals, partnering with Compliance to refine detection rules and ensure accurate alerting.

You will build Python and SQL pipelines for data prep and feature engineering, evaluate models for alert quality, and contribute to experimentation infrastructure for future ML initiatives.

Qualifications

• 7+ years of data analysis, data science, or data management in fintech/crypto/financial services or gambling.
• Experience with compliance, fraud, funding, finance, and customer support teams on large datasets.

Responsibilities

• Own the logic and rule-setting behind transaction monitoring across deposits and withdrawals.
• Translate compliance requirements into production detection logic with correct alert eligibility, thresholds, and deduplication.
• Develop and maintain Python and SQL pipelines for data prep, feature engineering, and detection workflows.
• Evaluate detection rules and models for coverage, alert quality, and threshold sensitivity.
• Design reproducible evaluations on historical data considering limited labels and shifts in behavior.
• Oversee testing, deployment, monitoring, and ops for detection workflows and data quality issues.

Skills

Python

SQL

Data analysis

Statistics

Problem solving

Education

Bachelor's degree in analytics/statistics/CS

Tools

dbt

Airflow

Tableau

Power BI

Hex

Athena

Trino

Kafka

Kubernetes

S3

Job description

Kraken is seeking a Sr. Data Analyst, Compliance to join our team remotely. You will own end-to-end data management and the logic behind transaction monitoring across deposits and withdrawals, partnering with Compliance to refine detection rules and ensure accurate alerting.

You will build Python and SQL pipelines for data prep and feature engineering, evaluate models for alert quality, and contribute to experimentation infrastructure for future ML initiatives.