Amazon · amazon.jobs · checked today
Sr. Data Engineer - Amazon Leo, Leo Global Business
Amazon Leo is an initiative to increase global broadband access through a constellation of 3,236 satellites in low Earth orbit (LEO). Its mission is to bring fast, affordable broadband to unserved and underserved communities around the world. Leo will help close the digital divide by delivering fast, affordable broadband to a wide range of customers, including consumers, businesses, government agencies, and other organizations operating in places without reliable connectivity.
Skills, with evidence
- Data modelling
You will design the data models used by these teams to efficiently dive deep into the business and extract insights.
must have - Python
Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
must have - SQL
Experience with SQL
must have - Spark
Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
must have - Warehousing
Experience operating large data warehouses
must have - AWS
You will be working directly on AWS to build internal tools used across the hundreds of Business Intelligence Engineers and Scientists to build business-critical reports and ML Models.
not practised here - Java
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
not practised here - Machine learning
You will be working directly on AWS to build internal tools used across the hundreds of Business Intelligence Engineers and Scientists to build business-critical reports and ML Models.
not practised here - Scala
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
not practised here
Your plan
- The SQL screen: correct, then fast≈ 2 h
SQL
- 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
- Keep only the latest row for each eventIntermediate
- Pivot a long metrics table to wideIntermediate
- 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
- Spark: read the plan Spark actually ran≈ 2 h
Spark
- broadcast() with auto-broadcast off, and the case where Spark ignores itIntermediate
- autoBroadcastJoinThreshold compares an estimate: flip a join with select() and one settingIntermediate
- Does Spark really run your EXISTS subquery once per row?Intermediate
- left_semi and left_anti: "customers who did / never did" without a full joinIntermediate
- A self-join on a real key that still multiplies rowsIntermediate
- Say it out loud≈ 1 h
20 drills · Intermediate + Advanced≈ 10 hours
Not covered by the plan: AWS, Java, Machine learning, Scala.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 10 dremoved the moment Amazon closes it