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Millions of Selling Partners trust Amazon's marketplace to grow their businesses, and hundreds of millions of customers depend on us every day. Behind that trust is a network of systems, tools, and workflows designed to detect and resolve fraud and abuse at scale — and our team builds them.
About the Role:
We are looking for a Sr. Process Engineer who thrives at the intersection of workflow engineering, risk operations, and tooling configuration in dynamic, variable environments. In this role, you will decompose complex and evolving investigation processes into streamlined, automated pathways — ensuring that every step that doesn't require human input is handled systematically, and every step that does is presented with precision and clarity.
You will design workflows that flex with changing risk landscapes rather than break under them. You will build tools that make expert investigators more effective by surfacing the right context at the right time so they can apply their judgment faster and more consistently, even when the case type is novel or the abuse pattern is emerging. You will own the end-to-end workflow lifecycle from requirements gathering through deployment, measurement, and iteration.
This role offers the opportunity to shape how Amazon protects seller and customer trust through innovative workflow engineering, intelligent automation, and adaptive risk operations.
- Key job responsibilities
- Own the design, configuration, and optimization of end-to-end investigation and intake workflows across centralized tooling infrastructure, translating risk policies and SOPs into scalable, adaptable solutions that perform in variable operational environments
- Decompose complex and evolving investigation processes into micro-task architectures that cleanly separate automated steps from human-judgment decision points, accounting for shifting risk signals and non-standard case types
- Develop API-driven tool enhancements that improve investigator efficiency, reduce handling time, and increase decision consistency across high-priority escalations
- Build configurable workflow components that enable rapid response to emerging abuse patterns, new risk categories, and changing enforcement policies without requiring full re-engineering
- Enable partner teams to onboard into centralized tooling platforms by defining integration patterns, configuration standards, and self-service documentation
- Conduct rigorous root cause analysis on workflow failures, systematic defects, escalation patterns, and false-positive/false-negative trends to identify and implement corrective actions
- Define, instrument, and monitor operational metrics to measure workflow effectiveness, automation coverage, defect reduction, and escalation resolution performance
- Drive continuous improvement by analyzing workflow performance data, identifying automation opportunities, and leading iteration cycles on tooling configurations
- Collaborate cross-functionally with engineering, product, risk science, operations, and policy teams to align workflow design with organizational priorities and risk mitigation objectives
- Influence roadmap prioritization by quantifying the operational impact of tooling gaps and presenting data-backed recommendations to leadership
About the team
The Res-Q (Risk Mitigation, Escalations Reduction, and Quality Assurance) team sits within Amazon's Customer and Partner Trust (CPT) organization. Our product, tooling, and data function provides centralized technology infrastructure for escalation teams across CPT operations, coordinating technology delivery across investigation, insights, intake, and detection product lanes. We build the tools and workflows that escalation teams depend on to resolve high-priority cases, eliminate systematic defects, and reduce seller-impacting incidents at scale.
Unlike fixed-process operations, this team operates in a variable environment where risk signals shift, abuse patterns evolve, and escalation types are rarely identical. The workflows we build must be adaptive, configurable, and resilient enough to handle emerging threats while maintaining speed and decision quality across thousands of cases weekly.
Basic Qualifications
- Bachelor's degree
- Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- Bachelor's degree, or 3+ years of workflow experience
- Experience that includes strong analytical skills, attention to detail, and effective communication abilities, or experience in software development and experience in managing and troublshooting network
- Experience in developing functional specifications, design verification plans and functional test procedures
Preferred Qualifications
- 5+ years of systems design, software development, operations, automation, and process improvement experience
- Experience working with large-scale data mining and reporting tools (i.e. SQL, MS Power Query, Python), or experience in building financial and operational reports/data sets that inform business decision-making
- Experience in one or more scripting languages (e.g., Python, Ruby, Perl)
- Experience in lean or six sigma methodologies for operational, process, and performance improvement projects including process mapping and process re-design
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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Amazonについて

Amazon
PublicAmazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.
10,001+
従業員数
Seattle
本社所在地
$1.5T
企業価値
レビュー
2.9
10件のレビュー
ワークライフバランス
2.8
報酬
3.7
企業文化
2.5
キャリア
2.3
経営陣
2.1
35%
友人に勧める
良い点
Good pay and compensation
Strong benefits package
Flexible scheduling options
改善点
Poor management and leadership
Limited growth and promotion opportunities
High stress and demanding work environment
給与レンジ
4件のデータ
L2
L3
L4
L5
L6
L2 · Data Analyst L2
0件のレポート
$108,330
年収総額
基本給
$43,332
ストック
$54,165
ボーナス
$10,833
$75,831
$140,829
面接体験
10件の面接
難易度
3.7
/ 5
期間
21-35週間
内定率
20%
体験
ポジティブ 10%
普通 10%
ネガティブ 80%
面接プロセス
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Onsite/Virtual Loop
6
Team Matching
7
Offer
よくある質問
Coding/Algorithm
System Design
Behavioral/STAR
Leadership Principles
Technical Knowledge
ニュース&話題
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