Business Intelligence Engineer II, Advertising Finance Econ and Science
Amazon
DESCRIPTION
Drive the analytics engine behind Amazon’s fast-growing Ads business. As the Business Intelligence Engineer on our Economics & Science team, you’ll own the data pipelines and run analysis that reveal where the next wave of growth will come from.
- Mission: Equip senior leaders with science‑backed insights on long‑term ad revenue, store–ad interactions, and the initiatives that will lead the growth for the next year.
- Scope: Design large‑scale data systems, collaborate with economists and data scientists, and deliver models that forecast revenue, measure program impact, and guide business decisions.
- Impact: Your work informs strategy for ads business—and your findings are presented directly to VP‑ and SVP‑level stakeholders.
- Culture: High‑ownership, experimentation‑friendly environment where innovation is encouraged and scientific rigor matters.
- Growth: Work with top‑tier economists, data scientists, finance managers, gain deep exposure to both finance and data science, and see your analyses turn into real‑world decisions within weeks.
Key job responsibilities
- Translate ambiguous business questions into clear analytical requirements. Engage with economists, finance leaders, and product teams to scope the right data, metrics, and deliverables for each decision.
- Design, build, and own end‑to‑end data solutions. Develop scalable ETL pipelines, dimensional models, and automated QA checks that power dashboards, self‑service reports, and machine‑learning features.
- Deliver intuitive BI products. Create and maintain AWS QuickSight/Tableau dashboards and ad‑hoc analyses that surface daily performance, long‑term trends, and experiment results to stakeholders across Finance and Product.
- Enable science at scale. Provide high‑quality, documented data sets and feature stores that accelerate econometric and forecasting models—and partner with scientists to productionize their outputs.
- Mentor and upskill the org. Act as the senior SME for SQL, Python, and visualization best practices, raising the BI bar through code reviews, training sessions, and tooling guidance.
- Lead deep‑dive investigations. Dive deep into anomalies or emerging trends, quantify root causes and opportunities, and drive—or support—the implementation of data‑backed solutions that improve revenue, efficiency, or customer experience.