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Primary Technical Skills

Critical Technical Skills

Python (Required)

• Strong experience building applications, automation, analytical tooling, and fraudrelated data processing workflows.

AWS (Required)

• Hands-on experience with core AWS services for data processing and orchestration (Lambda, S3, Glue, Step Functions, EventBridge).
• Ability to design and scale fraudrelated data pipelines and event-driven architectures.

Visualization (Required)

• Advanced skills in Tableau and/or Python visualization libraries to support fraud insights, anomaly detection, and rule performance monitoring.

SQL (Required)

• Ability to write efficient queries for large and complex fraud or transaction datasets, including advanced joins, window functions, and performance tuning.

Primary Non-Technical Skills

Data Analyst, Specialist

Role Overview

The Senior Data Analyst, Specialist leads complex analytical and data engineering initiatives in support of Fraud Analytics and Fraud Operations across ES&F. This role is highly technical, combining advanced Python development, AWS data engineering, and fraudfocused analytical storytelling. The ideal candidate has significant fraud experience within financial services, banking, or fintech, and can build scalable analytical solutions that strengthen fraud prevention, detection, and operational efficiency.

Key Responsibilities

Advanced Data Engineering & Application Development

• Design, build, and maintain Python-based applications, tools, and automated analytical workflows that support fraud detection, monitoring, and reporting.
• Develop and manage AWS data pipelines (Lambda, S3, Glue, Step Functions, EventBridge) that process fraudrelated datasets at scale.
• Write production-quality code following best practices for testing, documentation, version control, CI/CD, and secure handling of fraud and transaction data.

Advanced Visualization, Storytelling & Reporting

• Build advanced, interactive dashboards using Tableau and/or Streamlit to monitor fraud trends, rule performance, operational KPIs, and emerging threats.
• Create Python-based visualizations (Plotly, Matplotlib, Seaborn) to support deep fraud analytics, pattern detection, and automated insights delivery.
• Translate complex fraud analytics and model outputs into clear, compelling narratives for senior and executive stakeholders.

Operational Ownership & Process Leadership

• Own key analytical and data engineering processes that support fraud detection, rule performance monitoring, and fraud operations reporting.
• Improve workflows through automation, optimization, and standardization to drive operational excellence.

Other Responsibilities

• Support fraudfocused special projects, deep dives, ad hoc analyses, and evolving business priorities.
• Perform additional duties as needed.

Required Qualifications

• Minimum five years of experience in data analytics, data engineering, or related technical roles.

Required fraud experience:

• Experience with fraud models, fraud rules performance analysis, earlylife monitoring, or fraud detection concepts.
• Background in financial services, banking, or fintech environments.
• Proven experience designing and developing Python applications and building AWS-based data pipelines.
• Strong analytical, proble msolving, and communication skills.
• Undergraduate degree or equivalent combination of education and experience.
• Required Fraud Related Skills
• Experience with fraud models, rules performance monitoring, or earlylife strategy performance.
• Familiarity with fraud taxonomies, digital fraud patterns, authentication risks, and fraud operations workflows.
• Experience interpreting behavioral, device, or metadata patterns associated with fraud activity.

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