Amazon · amazon.jobs · checked today
Sr. Business Intelligence Engineer, AWS Analytics Engineering
Business Intelligence Engineer to join the AWS Analytics Engineering (AAE) team supporting Amazon Quick. Formerly Amazon QuickSight, Quick has evolved from a standalone BI service into a comprehensive, generative-AI-powered business intelligence platform that combines traditional analytics with modern AI assistance. It brings together two complementary experiences.
Skills, with evidence
- Data modelling
- Experience with data modeling, warehousing and building ETL pipelines
must have - SQL
- 5+ years of SQL experience
must have - Warehousing
- Experience with data modeling, warehousing and building ETL pipelines
must have - AWS
- Experience with AWS technologies
must have · not practised here - Python
- Experience in scripting for automation (e.g. Python) and advanced SQL skills.
- Dashboards & BI
Amazon Quick Sight is the cloud-native dashboarding and visualization engine that powers governed datasets, interactive dashboards, and ML-driven insights such as forecasting and anomaly detection.
not practised here - Data quality
Amazon Quick Sight is the cloud-native dashboarding and visualization engine that powers governed datasets, interactive dashboards, and ML-driven insights such as forecasting and anomaly detection.
- Machine learning
Available in the browser, as a desktop companion, and through extensions for Slack and Microsoft Office, Quick is used to turn data into decisions and actions, without requiring machine learning expertise.
not practised here
Your plan
- The SQL screen: correct, then fast≈ 2 h
SQL · Data quality
- Median delivery time per cityIntermediate
- Bucket deliveries into quartilesIntermediate
- Median order value without a median functionIntermediate
- New and repeat orders by monthIntermediate
- Every order against its customer's averageIntermediate
- Python: the data-wrangling round≈ 3 h
Python
- Diff two snapshots of a tableIntermediate
- Explode an array column into rowsIntermediate
- Flatten nested event payloadsIntermediate
- Pivot a long metrics table to wideIntermediate
- Choose what an incremental run should readIntermediate
- Data modelling: the round most people fail≈ 3 h
Data modelling · Warehousing
- Addresses that stay true to the pastIntermediate
- Seat holds and the release-night raceIntermediate
- Subscription warehouse grainIntermediate
- Campaign efficiencyIntermediate
- Catalogue: products, variants and sellersIntermediate
- Say it out loud≈ 1 h
15 drills · Intermediate + Advanced≈ 8 hours
Not covered by the plan: AWS, Dashboards & BI, Machine learning.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 4 dremoved the moment Amazon closes it