Leads data engineering for AI and machine learning fraud-detection products. Responsibilities include integrating disparate data sources, building scalable and automated pipelines, supporting modeling workflows, deploying production artifacts, creating documented data products, improving reusable processes, collaborating cross-functionally, leading projects, and mentoring junior engineers. Required technologies include Python, PySpark, Airflow, Hadoop, SQL, Databricks, Linux/Bash, CI/CD, and JIRA.