• Own data platform health and pipeline performance with defined SLAs (freshness, completeness, correctness) • Implement modern data observability (monitoring for volume anomalies, freshness gaps, schema changes, and pipeline failures) • Lead incident response and root-cause workflows, partnering with Engineering on cross-system fixes • Define scalable data architecture (warehouse/lakehouse/hybrid) and canonical, governed datasets • Own HIPAA-aware web & product analytics instrumentation, event taxonomy, and QA standards • Uplevel BI dashboard performance, segmentation, and metric governance (semantic layer, documented sources of truth, fast load times) • Provide technical leadership for customer data integrations, partnering daily with Customer Success, Data, and Engineering • Establish lightweight governance: metric definitions, access controls, documentation, and ownership • Mentor team members while shaping the future structure of the data & analytics function

• Analyze, evaluate, and improve the quality of multilingual text classifier outputs. • Partner with linguistics and engineering teams to develop and refine language-specific parsers. • Translate and validate taxonomy content such as Skills, Titles, and Occupations from English to Mandarin h (and/or Mandarin to English as needed). • Identify patterns in data errors and contribute to ongoing improvements in rules, logic, and workflows. • Document findings clearly and follow established processes to ensure consistency and quality.

• Monitor transactions and customer accounts to identify and investigate suspicious or fraudulent activity. • Utilize various fraud detection tools, systems, and techniques to identify emerging fraud trends and patterns. • Conduct in-depth investigations into suspected fraudulent activities, gathering evidence and documenting findings. • Analyze large datasets using SQL to identify anomalies, patterns, and insights related to fraudulent behavior. • Develop and implement fraud prevention rules, strategies, and controls. • Stay up-to-date with industry best practices, emerging fraud threats, and new technologies in fraud detection and prevention.

• Monitor transactions and customer accounts to identify and investigate suspicious or fraudulent activity. • Utilize various fraud detection tools, systems, and techniques to identify emerging fraud trends and patterns. • Conduct in-depth investigations into suspected fraudulent activities, gathering evidence and documenting findings. • Analyze large datasets using SQL to identify anomalies, patterns, and insights related to fraudulent behavior. • Develop and implement fraud prevention rules, strategies, and controls. • Stay up-to-date with industry best practices, emerging fraud threats, and new technologies in fraud detection and prevention.