Description We are seeking a talented Business Intelligence Analyst II to contribute to building and maintaining our data environment, driving insights, and supporting the execution of fraud detection rules.Key Responsibilities:
Build and maintain underlying data sets to support the development of new fraud strategies and real-time detection rules.
Contribute to the creation and maintenance of reusable data pipelines, focusing on feature creation and dataset design for fraud detection rule performance.
Assist in developing the data environment needed for fraud detection and KPI reporting, including defining and calculating key performance metrics.
Design datasets and dashboards for analyzing and monitoring new fraud detection rule performance and establishing alerting thresholds.
Define strategies for monitoring key components of data pipelines, proactively detecting issues that may impact critical processes.
Requirements
Requirements:
Strong understanding of data modeling principles and experience with data integration and ETL (Extract, Transform, Load) processes.
Proficiency in SQL for querying databases and creating curated datasets for reporting and analytics.
Hands-on experience using BigQuery and expertise in designing interactive dashboards using Looker and Tableau.
Experience and proficiency with GitHub for version control.
Strong analytical skills to interpret data, derive insights, and define key performance indicators (KPIs).
Understanding of retail banking, with a background in fraud or risk management considered ideal.
Familiarity with data models used in financial services organizations is a plus.
2+ years of programming knowledge and experience with Python, SQL, SCALA, and ANVIL.
Preferred Skills:
Ability to develop dashboards for monitoring fraud control performance, including account opening rule performance, debit authorization rule performance, and offline transaction detection processes.
Experience defining and calculating performance metrics for fraud detection and establishing thresholds for alerting when metrics deviate from expected performance bounds.
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