Business Intelligence Engineer, AWS Field Exp - BPO

Description

Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform, offering over 100 fully featured services to millions of active customers globally, including the fastest-growing startups, largest enterprises, and leading government agencies.

Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform, offering over 100 fully featured services to millions of active customers globally, including the fastest-growing startups, largest enterprises, and leading government agencies. Business Product & Operations (BPO), part of the AWS Field Experience (AFX) organization, builds the data infrastructure and automation systems that power Sales, Marketing, and Global Services (SMGS) Operations.

The team owns analytics platforms, data pipelines, and measurement frameworks that enable operational teams to quantify automation impact and make data-driven decisions  are looking for a Business Intelligence Engineer who will own the measurement and analytics layer for agentic AI automation across BPO operations. This role sits at the intersection of data engineering, business intelligence, and AI operations. You will quantify the impact of AI-driven automation on operational workflows, build and maintain the data pipelines that feed reporting and decision-making, and partner directly with internal customers to deliver actionable insights.

This is not a traditional BI role. The right candidate leans toward technical platform work: building reliable data pipelines, managing scheduling infrastructure, troubleshooting ETL failures, and improving data quality at the source. You will operate in a fast-moving environment where automation capabilities evolve weekly and measurement frameworks must keep pace.

Design, implement, and deliver BI solutions that measure the impact of agentic AI automation on operational efficiency, accuracy, and throughput

Identify opportunities and define key metrics to evaluate automation effectiveness and drive business strategy

Analyze and solve problems at their root, stepping back to understand the broader context of how AI-driven workflows interact with human operations

Build and maintain data pipelines (ETL/ELT), scheduling infrastructure, and orchestration systems that power downstream analytics and reporting

Conduct deep-dive analyses of automation performance, formulate conclusions, and present recommendations to leadership

Produce written insights for stakeholders that shape metric development, automation investment decisions, and operational reporting

Simplify and automate reporting, audits, and other data-driven activities, leveraging AI tooling where applicable

Partner with Data Engineering and platform teams to enhance data infrastructure, data availability, and broad access to automation and customer insights

Develop and drive best practices in data integrity, pipeline reliability, validation, and documentation

Instrument AI automation systems for measurable outcomes, working with engineering teams to close observability gaps

Support internal customers across SMGS Operations with analytics solutions (dashboards, ad‑hoc analyses, self‑service tools)

Learn and adopt new technologies and techniques to meaningfully support product and process innovation in AI-driven operations

Measuring agentic AI automation outcomes: throughput, accuracy, cycle time reduction, and customer impact across BPO operations

Owning and operating data pipelines in Redshift/Glue/Airflow that feed executive dashboards and operational reporting

Supporting internal customers (operations managers, program leads) with ad‑hoc analysis and self‑service analytics

Pipeline scheduling,…