Data Engineer , Amazon Customer Service

Customer Experience Products (CXP) is part of Amazon's Customer Service (CS) organization, responsible for the data infrastructure that powers measurement, analytics, and automation across every customer service interaction — chat, voice, bots, and digital self-service. Our Data Engineers own the foundational data layer that BIEs, scientists, and product teams rely on to evaluate performance, shape OP planning, and drive customer-facing product improvements. You will join a team of data engineers, BIEs, and analysts who build and maintain the pipelines, schemas, and data platforms that underpin CXP's analytics ecosystem. The data you deliver directly shapes how Amazon understands and improves the customer service experience at scale — from customer journey funnels and resolver efficacy measurement to WBR automation and contact-reduction quantification. The team currently drives high-impact data engineering initiatives including migration to Gold Schema datasets, Datanet-to-Andes pipeline modernization, Panorama table consolidation, and cross-channel metrics unification. You will own meaningful data infrastructure workstreams from day one. Key job responsibilities - Design and implement logical and physical data models for complex, large-scale datasets that drive downstream analytics, WBR reporting, and self-service BI infrastructure across CXP verticals (CFS, CX-STAR, Concessions/CAP). - Build and optimize data pipelines (ETL/ELT) for difficult and large-scale datasets using technologies such as AWS Glue, Spark, Redshift, and EMR. Own pipeline reliability for business-critical reporting surfaces including VP-level dashboards and weekly business review decks. - Own data quality end-to-end. Establish SLAs, define data certification standards, build monitoring and alerting for pipeline health, and proactively identify and resolve data quality gaps (e.g., upstream DQ issues in Panorama tables, source data discrepancies). - Drive migration and modernization of data infrastructure. Lead migration of team-owned objects to dedicated schemas (e.g., Datanet-to-Andes migration), consolidate reporting tables to eliminate redundant queries, and align data sources to Gold Schema standards for consistent, auditable metrics. - Improve self-service access to data. Build tools and processes for data lineage tracking, discoverability, and governance. Reduce manual reporting overhead by engineering automated solutions that enable analysts and PMs to self-serve. - Partner cross-functionally with SDEs, BIEs, scientists, and PMs to understand data needs, propose solutions, and deliver datasets that enable stakeholders to make data-driven decisions. Integrate data solutions into broader team architecture and ensure alignment with CS Data & AI team dependencies. - Automate manual processes and improve operational excellence. Improve code quality, dependency management, and pipeline observability. Reduce BIE bandwidth consumed by manual data preparation work. - Mentor and develop peers. Participate in hiring, technical assessments, and code reviews. Raise the bar on data engineering practices across the team. A day in the life You might start your morning validating a pipeline migration that consolidates conversation and interaction ID attributes into a unified Panorama reporting table — eliminating the need for analysts to query multiple sources. After standup, you investigate a data quality gap in an upstream concessions table, root-cause the issue, and coordinate with the source team on a fix. In the afternoon, you build a new ETL job to power a customer journey funnel dataset that tracks the full path from CSHP entry through bot interaction to resolution. Before end of day, you review a teammate's pipeline code and help optimize a Spark job that's approaching its SLA window. We thrive on solving challenging data problems to build the infrastructure our customers — the analysts, scientists, and PMs who drive CS improvements — depend on