Engineering Analyst

Google•Seattle, WA

11h•$132,000 - $190,000•Hybrid

About The Position

The Engineering Analyst will work on Google’s Trust and Safety team, which reduces risk and protects the experience of users and business partners across Google products. The team protects Google’s integrity by combating spam, fraud, and abuse; developing and communicating product policies; and collaborating with Engineering, Legal, Public Policy, and Sales Engineering to address abuse at scale, often through industry-wide solutions. The role uses technical expertise, user insights, and proactive communication to advance quality and safety standards. The position involves analyzing and interpreting data, identifying and mitigating fraud, evaluating machine-learning models and classifiers, developing hypotheses and metrics for ambiguous problems, and improving automated tools and systems. It reports to a Google office and may allow a hybrid schedule under Google policy. The US base salary range is $132,000–$190,000, with a 15% bonus target, equity, and benefits; individual compensation depends on job-related skills, experience, and relevant education or training.

Requirements

Bachelor’s degree in Computer Science, Data Science, Software Engineering, Statistics, Mathematics, or a related field.
At least 2 years of experience in the job offered or in an Engineering Analyst-related occupation.
At least 2 years of experience with data analysis and synthesis to identify trends, generate summary statistics, and derive insights from quantitative and qualitative data.
At least 2 years of experience with data visualization using charts, graphs, and tables to capture and share data insights.
At least 2 years of experience designing technical solutions that translate complex business requirements into robust technical blueprints aligned with architectural standards.
At least 2 years of experience navigating technical infrastructure to communicate system behaviors, capabilities, and limitations with engineering partners.
At least 2 years of experience processing and manipulating large volumes of data for product-related reporting and decision-making.

Responsibilities

Construct accurate and efficient medium-complexity queries, using statistical methods to interpret data and solve problems.
Identify and prevent fraud by using domain expertise to uncover system gaps, source additional training examples at scale, and mitigate abuse.
Compute performance statistics for complex machine-learning models and classifiers to evaluate technical effectiveness, identify biases, and resolve classification issues.
Generate and test hypotheses for ambiguous challenges by defining quantitative metrics and extracting creative signals for algorithm improvement and feature engineering.
Improve automated tools and systems through technical analysis, independently interpreting complex data, and presenting actionable insights to diverse stakeholders.