DE - RCE - Fraud Analytics - Senior Professional

EY; Bengaluru; On-site; INR 900,000 - 1,400,000; Full time

Job summary

EY is seeking a Senior Data Analyst, Specialist to drive advanced analytics and data engineering for Fraud Analytics and Fraud Operations across ESF. The role combines Python development, AWS data pipelines, and fraud-focused storytelling to strengthen detection and efficiency.

The ideal candidate has solid fraud experience in financial services, banking, or fintech and can build scalable solutions with Tableau and Python visuals. Bengaluru-based position with global impact.

Qualifications

• Minimum five years of experience in data analytics, data engineering, or related technical roles.
• Experience with fraud models, rules performance analysis, and monitoring concepts.
• Background in financial services, banking, or fintech environments.
• Proven Python application development and AWS data pipelines.

Responsibilities

• Design, build, and maintain Python-based applications and automated workflows for fraud detection and reporting.
• Develop and manage AWS data pipelines that process fraud-related datasets at scale.
• Create advanced dashboards using Tableau and Python visualizations for fraud insights and KPIs.
• Translate complex analytics into clear narratives for senior stakeholders.
• Own analytical and data engineering processes supporting fraud detection and operations.

Skills

Python

AWS

Visualization

SQL

Education

Undergraduate degree or equivalent combination of education and experience

Tools

Tableau

Streamlit

Plotly

Matplotlib

Job description

Role Overview

At EY, you ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

The Senior Data Analyst, Specialist leads complex analytical and data engineering initiatives in support of Fraud Analytics and Fraud Operations across ESF. This role is highly technical, combining advanced Python development, AWS data engineering, and fraud-focused 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.

Primary Technical Skills

• Python (Required) – Strong experience building applications, automation, analytical tooling, and fraud‑related 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 fraud‑related 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.

Key Responsibilities

• 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 fraud-related 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.
• 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.
• 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.
• Support fraud‑focused 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.
• Experience with fraud models, fraud rules performance analysis, early‑life 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, problem‑solving, and communication skills.
• Undergraduate degree or equivalent combination of education and experience.
• Familiarity with fraud taxonomies, digital fraud patterns, authentication risks, and fraud operations workflows.
• Experience interpreting behavioral, device, or metadata patterns associated with fraud activity.