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Engineering Analyst, Payments

Google

Bengaluru, Karnataka, IndiaOn Site

Original Job Summary

About the Role

The Engineering Analyst, Payments at Google is focused on identifying and mitigating payment fraud and abuse to ensure a secure environment for users. Working on the Trust & Safety team, you will collaborate with engineers and product managers to analyze data, identify trends, and develop solutions to fight fraud across Google products.

Key Responsibilities

  • Investigate fraud and abuse incidents to identify patterns
  • Perform statistical analysis on payments and risk data
  • Collaborate with Engineering and Product teams to enhance tools and signals
  • Assess risk and vulnerability of products and features
  • Participate in on-call rotation for managing escalations

Qualifications

  • Bachelor's degree or equivalent practical experience required
  • Minimum of 2 years in project management and data analysis
  • Experience with SQL, R, Python, or C++ preferred
  • Technical skills in machine learning systems an advantage
  • Excellent problem-solving and communication skills

About Google Trust & Safety

Google's Trust & Safety team is dedicated to protecting users and partners by mitigating risks related to abuse such as fraud, malware, spam, and account hijacking. This cross-functional team works globally to maintain trust in Google products.

Key skills/competency

  • Data Analysis
  • SQL
  • Python
  • Machine Learning
  • Project Management
  • Fraud Detection
  • Risk Assessment
  • Process Optimization
  • Communication
  • Problem Solving

How to Get Hired at Google

🎯 Tips for Getting Hired

  • Customize your resume: Highlight data analytics and fraud skills.
  • Research Google: Understand their Trust & Safety initiatives.
  • Prepare for technical tests: Practice SQL, Python, and statistical methods.
  • Showcase project experience: Share real examples of process optimization.

📝 Interview Preparation Advice

Technical Preparation

Review SQL queries and data sets.
Practice Python coding challenges.
Study machine learning basics.
Analyze sample fraud case studies.

Behavioral Questions

Describe a time you solved a process issue.
Explain managing cross-functional project conflict.
Detail handling high-pressure escalations.
Discuss adapting to rapid changes.