Amazon · amazon.jobs · checked today
Data Engineer, Global Procurement Technology, Global Procurement Tech
Data is at the heart of procurement. Do you want to make that data AI-ready — powering not just insight but autonomous decision-making and action at a global scale? As a Data Engineer on the Global Procurement Technology team, you will build the data foundation that drives AI transformation across Amazon's procurement organization.
Skills, with evidence
- Data modelling
Experience with data modeling, warehousing and building ETL pipelines
must have - SQL
Experience with SQL
must have - Spark
Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
must have - AWS
Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
must have · not practised here - Data quality
- Create logical and physical data models across large datasets from multiple internal and external sources, ensuring data quality and consistency.
- Python
- Experience programming with at least one modern language such as C++, C#, Java, Python, Golang, PowerShell, Ruby
- Streaming
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Go
- Experience programming with at least one modern language such as C++, C#, Java, Python, Golang, PowerShell, Ruby
not practised here - Java
- Experience programming with at least one modern language such as C++, C#, Java, Python, Golang, PowerShell, Ruby
not practised here
Your plan
- The SQL screen: correct, then fast≈ 1 h
SQL · Data quality
- Average order value by countryFoundations
- Line items per orderFoundations
- Revenue by buyer countryFoundations
- Customers who never orderedFoundations
- Hiring funnel by roleFoundations
- 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
- Remove repeats from a sorted list of partitionsFoundations
- Data modelling: the round most people fail≈ 2 h
Data modelling
- Marketplace core entitiesFoundations
- Cinema seat bookingFoundations
- City parking baysFoundations
- Dating app matchesFoundations
- Food delivery ordersFoundations
- Pipeline design: safe to run twice≈ 4 h
Streaming
- Which day does it belong to?Foundations
- Parcel tracking pipelineIntermediate
- Five minutes behind the sourceIntermediate
- The source will not let youIntermediate
- The fact arrived firstIntermediate
- 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≈ 11 hours
Not covered by the plan: AWS, Go, Java.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 11 dremoved the moment Amazon closes it