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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Business Intelligence Engineer, Alexa Endpoints - **Company:** Amazon.com, Inc. - **Location:** Bellevue, WA, United States - **Experience:** Experienced - **Salary:** $99,500.0 - $160,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Amazon Alexa, Amazon Elastic Compute Cloud, Amazon S3, Data Analysis, Big Data, Databases, Extract Transform Load (ETL), Data Mining, Data Visualization, Data Warehousing, Amazon DynamoDB, R (Programming Language), Mobile Application Software, Python (Programming Language), MATLAB, NoSQL, Oracle (Applications), SAS (Software), SQL Databases, Tableau (Software), Data Processing, Scripting, Business Intelligence Development Studio, Advanced Reports, Generative AI, Data Lakes, Virtual Agents, Amazon Redshift - **Published:** August 18, 2026 - **Apply:** https://www.jobmonkeyjobs.com/career/27943564/Business-Intelligence-Engineer-Alexa-Endpoints-Washington-Bellevue-7375 ## About the Role 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience - Experience with data visualization using Tableau, Quicksight, or similar tools - Experience with data modeling, warehousing and building ETL pipelines - Experience in Statistical Analysis packages such as R, SAS and Matlab - Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling Preferred Qualifications - Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift - Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets - Knowledge of product experimentation (A/B testing) - Experience with consumer or subscription product analytics, including adoption, engagement, retention, and cohort analysis - Fluency with Generative AI copilots/agents to accelerate analytics work, and interest in building agentic and self-service BI tooling ## Description Alexa Mobile BI (AMBI) owns the analytics that measure the mobile success of Alexa+, the next generation of Alexa. As a Business Intelligence Engineer on this team, you will define what success looks like for a brand-new product still being shaped: how customers discover it, adopt it, engage with it day over day, and come back. Your work goes straight to the people making the calls. You will build the metrics, deep-dives, and dashboards that Product Managers and senior leadership use to set the mobile roadmap and make launch go/no-go decisions. An analysis you run this week can change what ships next month. We are also rebuilding how BI gets done. AMBI is investing in Generative and Agentic AI: AI agents that write SQL, automate recurring reporting, and stand up dashboards, plus natural-language, self-service analytics so PMs and leaders can answer their own questions instead of waiting in a queue. We want a builder who helps design these workflows, not just consume them. The ideal candidate is a self-starter with a strong bias for action, a communicator who can turn ambiguity into clear metrics, and an AI power user (or someone eager to become one) who uses modern tooling to raise the whole team's velocity. If you are excited by data, energized by a fast-moving launch, and want to own an analytics domain end-to-end with room to grow, this role is for you., * Own the metrics, datasets, and analytics for Alexa+ on mobile across adoption, entitlement, engagement, and retention, defining success measures for a product that is still being built. * Run deep-dive cohort and retention studies (retention curves, customer lifecycle stage, and lapse analysis) across voice, type, and tap engagement, plus feature-adoption studies that explain why the numbers move. * Build and maintain exec-facing business reviews and QuickSight dashboards that leadership relies on every week. * Partner directly with Product Managers and leadership to translate open questions into metrics, and to inform roadmap and launch (go/no-go) decisions. * Evaluate and interpret experimentation results. Measure the customer impact of A/B tests and feature experiments, and translate them into clear, evidence-based recommendations for launch and rollout decisions. * Help design and adopt agentic analytics workflows such as AI agents that generate SQL, automate reporting, and power natural-language self-service for stakeholders. * Build ETL and data models on the Data Lake/Warehouse (via Datanet/Cradle) and own data quality and metric consistency so every number tells the same, trusted story. A day in the life A PM pings you before the business review (BR): Alexa+ engagement dipped in one cohort. Is it real, or a reporting artifact? You pull the thread with a retention deep-dive, confirm it's a genuine early-lifecycle drop-off, and quantify it by modality. By mid-morning you've drafted the finding for the BR narrative and flagged it to the feature PM. In the afternoon you turn the one-off analysis into a reusable, self-service dashboard so the team can watch the cohort without asking you again, and you lean on the team's AI tooling to scaffold the SQL and the first pass of the build, so you spend your time on judgment, not boilerplate. You experiment, move fast, and keep learning. About the team Alexa Mobile BI (AMBI) is the analytics engine for the Alexa Mobile App. We measure engagement, retention, and the growth of Alexa+, and we are the source of truth for the WBR/MBR reporting that Product and leadership run the business on. We work across the full stack, from the Data Lake/Warehouse and Datanet/Cradle pipelines to QuickSight, and we are reinventing that stack with Generative and Agentic AI to move faster and go deeper than a traditional BI team. We move fast, experiment constantly, and balance strong technical craft with the business judgment to know which questions actually matter. This is a high-visibility product at a pivotal moment, and we want an ambitious engineer who's ready to grow their scope alongside it. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Leveraging Large Language Models for Legacy Code Translation: Challenges and Solutions](https://www.wearedevelopers.com/videos/1157-leveraging-large-language-models-for-legacy-code-translation-challenges-and-solutions) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Building a hypercar from scratch](https://www.wearedevelopers.com/videos/607-building-a-hypercar-from-scratch) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)