Senior BI Engineer — Agentic Partner Analytics

Description

AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services.

The Partner Analytics & Reporting Knowledge (PARK) team, within the AWS Specialists & Partners Operations Org (ASP Ops), has an immediate need for a creative and driven Sr Business Intelligence Engineer (BIE).

Key job responsibilities

As a Sr Business Intelligence Engineer on this team, you will design and build agentic reporting solutions that go beyond static dashboards — creating intelligent, extensible data artifacts that surface actionable insights autonomously. You will embed deeply with business stakeholders across the AWS Partner organization to understand their highest-priority questions, then engineer reporting workloads that answer those questions proactively, at scale.

You are technically versatile (ETL, Python, SQL, visualization) and relentlessly focused on business impact. Your deliverables will accelerate the partner organization by identifying key drivers behind top-line metrics and translating them into clear next steps for sales and leadership teams.

Key Job Responsibilities

Build agentic reporting solutions — Design, develop, and operate intelligent reporting workloads that autonomously generate, refresh, and distribute actionable data artifacts across the partner organization.

Create extensible, actionable data artifacts — Engineer centralized reporting assets (metrics, dashboards, automated analyses) built for reuse by peer BI teams, with clear interfaces and scalable architecture.

Get closer to the business — Partner directly with executive leadership and geographically based sales teams to deeply understand their operating rhythms, KPIs, and decision points, then build reporting that maps precisely to those needs.

Drive insight-to-action — Go beyond descriptive reporting: proactively surface recommendations grounded in statistical analysis, cross-referenced data sources, and industry benchmarks.

Own metrics and standardization — Define, develop, and maintain business-critical metrics and reports across all business levers, KPIs, and financials; drive alignment and consistency across global sales teams and executive reporting.

Advance AI/ML-powered reporting — Apply Generative AI and agentic AI patterns to raise the bar on reporting automation, insight generation, and stakeholder self-service across the partner organization.

Provide thought leadership — Lead global business deep dives, communicate findings to executive management, and shape the analytical strategy for the organization.

Inform data infrastructure — Provide data engineering teams with functional and technical requirements to improve pipeline efficiency, data quality, and the extensibility of the underlying data platform.

Partner cross-functionally — Collaborate with engineering, product, business, and finance teams to create KPIs, measurement methodologies, and scalable analytical frameworks.

A day in the life

Your primary focus is building intelligent, agentic reporting solutions that put the right insight in front of the right person, without anyone having to ask for it. You'll work closely with executive leadership and geographically based sales teams to understand the questions they're actually trying to answer, then design central reporting that answers those questions proactively and  people you build for are busy;

they need data that tells them what to do next, not just what happened last quarter.

A big part of the role is making sure the things you build are reusable. Other BI teams across the organization will pick up your reporting artifacts and plug them into their own workflows, so you'll spend real time thinking about how to structure outputs so they're extensible and self-serve. You'll also go beyond the regular…