Behind every seamless customer service chat, voice bot, and digital self-service interaction at Amazon is a massive, highly complex data engine. The Customer Experience Products (CXP) team is currently scaling that foundational data layer, and they are looking for a sharp Data Engineer to join the core analytics ecosystem in Seattle, WA.
If you’ve been searching for a role where you can ditch the repetitive maintenance tasks and take immediate ownership of large-scale ETL pipelines, schema migrations, and real-time reporting infrastructure, this opportunity on the Amazon Customer Service team is worth a close look. Navigating the tech industry right now means finding teams tackling genuine distributed systems challenges—and for engineers exploring high-impact openings across amazon careers, this position places you right at the intersection of big data, cloud architecture, and operational excellence.
About Amazon Customer Service (CXP): Powering Global Support
Before submitting your profile, it helps to understand the massive scale and technical scope driving the organization:
- The Foundational Data Layer: CXP owns the data infrastructure powering measurement, analytics, and automation across every customer service interaction—spanning chat, voice, bots, and digital self-service.
- Modernization & Scale: The team currently drives critical data modernization initiatives, including migration to Gold Schema datasets, Data net-to-Andes pipeline modernization, Panorama table consolidation, and cross-channel metrics unification.
- Culture & Innovation: Amazon embraces diverse career paths, continuous learning, and work-life harmony, ensuring engineers feel supported while tackling complex distributed systems challenges.
Quick Job Overview
| Job Metric | Details |
| Company | Amazon |
| Job ID | 10547654 |
| Role Title | Data Engineer, Amazon Customer Service |
| Field of Work | Data Engineering & Cloud Data Infrastructure |
| Employment Type | Full-Time |
| Work Location | Seattle, WA (USA) |
| Pay Range | $132,100 – $178,800 annually (base salary; total package includes sign-on payments and RSUs) |
What Does a Data Engineer at Amazon Customer Service Do?
As a Data Engineer on this team, you will own meaningful data infrastructure workstreams from day one, building the pipelines and schemas that business intelligence engineers (BIEs), scientists, and product teams rely on.
Core responsibilities include:
- Data Modeling & Architecture: Designing and implementing logical and physical data models for complex, large-scale datasets driving downstream analytics, WBR reporting, and self-service BI infrastructure across CXP verticals.
- ETL/ELT Pipeline Optimization: Building and optimizing data pipelines for massive datasets using technologies like AWS Glue, Spark, Redshift, and EMR, ensuring strict pipeline reliability for VP-level dashboards.
- End-to-End Data Quality: Establishing SLAs, defining certification standards, building monitoring systems, and proactively resolving upstream data quality gaps.
- Infrastructure Migration: Leading the migration of team-owned objects to dedicated schemas (such as Datanet-to-Andes) and consolidating reporting tables to align with Gold Schema standards.
- Cross-Functional Partnership: Collaborating closely with Software Development Engineers (SDEs), BIEs, scientists, and PMs to deliver auditable, high-performance data solutions.
Insider Guide: How to Ace the Amazon Data Engineering Interview Loop
💡Securing an engineering role at AWS requires demonstrating deep technical competency in distributed data systems, operational excellence, and alignment with Amazon’s Leadership Principles (such as “Dive Deep” and “Insist on the Highest Standards”). Keep these interview strategies in mind:
- Master Large-Scale ETL and Performance Optimization
Expect technical deep-dives into how you handle performance bottlenecks in distributed frameworks like Apache Spark, EMR, or AWS Glue. Be ready to discuss how you optimize slow-running queries, manage shuffle partitions, and handle schema evolution in data lakes.
- Showcase Data Governance and Pipeline Reliability
Amazon places a massive emphasis on data quality. Prepare behavioral stories where you caught silent data corruption, set up robust alerting SLAs, or led complex data migrations (like schema upgrades or data center platform shifts) with zero downtime for downstream reporting.
- Speak to Cross-Functional Collaboration
Data Engineers at Amazon don’t work in silos. Highlight examples of how you partnered with product managers, scientists, and analysts to translate vague business requirements into clean, production-ready dimensional models.
Candidate Qualifications & Required Skills
Amazon values diverse experiences and encourages applicants to apply even if they don’t meet every preferred qualification.
Basic Qualifications:
- 3+ years of professional data engineering experience.
- 1+ years of experience developing and operating large-scale data structures using ETL/ELT processes.
- 1+ years of experience working with OLAP technologies for business intelligence analytics.
- 1+ years of strong hands-on experience using SQL and Oracle databases.
- Bachelor’s degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field.
Preferred Qualifications:
- Deep practical experience with AWS cloud stack tools, including Redshift, S3, AWS Glue, EMR, Kinesis, Firehose, Lambda, and IAM security permissions.
- Working knowledge of non-relational databases and distributed data stores (document stores, key-value stores, or column-family databases).
How To Apply
If you are ready to scale cloud-level data systems with a global leader through amazon careers, tailor your resume to emphasize your large-scale ETL pipelines, data modeling expertise, and cross-functional project ownership. Head over to the official Amazon jobs portal, search for Job ID: 10547654, and submit your profile.
Good luck with your application! Let me know if you need help drafting interview prep notes or want to line up the next blog post.
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See you in the next blog, and all the best!
Frequently Asked Questions (FAQs)
- Where is this Data Engineer position located?
Ans: This role is based out of Amazon’s corporate hub in Seattle, Washington.
- What does the total compensation package look like?
Ans: The base salary range spans from $132,100 to $178,800 annually. Total compensation at Amazon also includes sign-on bonuses, Restricted Stock Units (RSUs), and robust health, retirement, and family-leave benefits.
- What technical stack is used in this role?
Ans: Engineers on this team frequently work with AWS Glue, Apache Spark, Redshift, EMR, SQL, and Oracle, building robust pipelines that feed into Amazon’s internal Gold Schema reporting layers.
- Do I need to meet 100% of the preferred qualifications to apply?
Ans: No! Amazon explicitly encourages candidates with unique backgrounds and skillsets to apply, even if they don’t check every single preferred box.
- How can I apply for Job ID 10547654?
Ans: You can submit your application by visiting the official Amazon Careers portal, searching for Job ID: 10547654, and uploading your resume.