Amazon · amazon.jobs · checked today
Data Engineer, Amazon Global Selling - AIT
Amazon Global Selling has been helping individuals and businesses increase sales and reach new customers around the globe. Today, more than 50% of Amazon's total unit sales come from third-party selection. The Global Selling team in China is responsible for recruiting local businesses to sell on Amazon’s 19+ overseas marketplaces and supporting local Sellers’ success and growth on the Amazon.
Skills, with evidence
- Data modelling
Experience with data modeling, warehousing and building ETL pipelines
must have - Python
Experience with one or more scripting language (e.g., Python, KornShell)
must have - SQL
Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
must have - Warehousing
Experience with data modeling, warehousing and building ETL pipelines
must have - Spark
Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
must have - Dashboards & BI
Develop automated data monitoring tools and interactive dashboards to enhance business teams’ insights into core metrics (e.g., user behavior, AI model performance).
not practised here - Failure handling
Develop automated data monitoring tools and interactive dashboards to enhance business teams’ insights into core metrics (e.g., user behavior, AI model performance).
- Streaming
Design and implement end-to-end data pipelines (ETL) to ensure efficient data collection, cleansing, transformation, and storage, supporting both real-time and offline analytics needs.
- Governance & security
Establish data standardization and governance policies to ensure consistency, accuracy, and compliance.
not practised here - Scala
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
not practised here
Your plan
- The SQL screen: correct, then fast≈ 1 h
SQL
- Line items per orderFoundations
- Average order value by countryFoundations
- Count at the right grainFoundations
- Revenue by buyer countryFoundations
- Join three tables into an order detailFoundations
- Python: the data-wrangling round≈ 2 h
Python
- Clean spreadsheet headers into column namesFoundations
- Events normalization jobFoundations
- List the distinct composite keysFoundations
- Deduplicate rows, first one winsFoundations
- Render a byte count for humansFoundations
- Data modelling: the round most people fail≈ 2 h
Data modelling · Warehousing
- Marketplace core entitiesFoundations
- Cinema seat bookingFoundations
- City parking baysFoundations
- Dating app matchesFoundations
- Food delivery ordersFoundations
- Pipeline design: safe to run twice≈ 3 h
Failure handling · Streaming
- After the first one finishesFoundations
- The history nobody keptFoundations
- The spreadsheet is a dependencyFoundations
- Waiting for the fileFoundations
- Where did the rows go?Foundations
- Spark: read the plan Spark actually ran≈ 1 h
Spark
- HAVING vs WHERE: where does a filter after groupBy actually run?Foundations
- countDistinct vs approx_count_distinct: what the extra shuffle buysFoundations
- COUNT(*) vs COUNT(column): the null trapFoundations
- Grouping by two columns: what changes in the shuffle?Foundations
- Does the join type change the join strategy?Foundations
- Say it out loud≈ 1 h
25 drills · Foundations + Intermediate≈ 10 hours
Not covered by the plan: Dashboards & BI, Governance & security, Scala.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 12 dremoved the moment Amazon closes it