Amazon · amazon.jobs · checked today
Data Engineer, Selling Partner Trust
As we strive to be Earth's most customer-centric company, Amazon has reinvented how hundreds of millions of people shop online – providing customers with the opportunity to find and discover virtually anything they want to buy and providing millions of sellers with a platform for growing successful businesses. We are looking for an exceptional data engineer to help us develop new ways to build trust and loyalty with sellers, a crucial component of our flywheel Sellers’ trust in Amazon is our top priority and in this role, you will be tasked with building that trust over time by building and ma
Skills, with evidence
- Data modelling
Experience with data modeling, warehousing and building ETL pipelines
must have - SQL
1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience
must have - Warehousing
Experience with data modeling, warehousing and building ETL pipelines
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 - Governance & security
In this role, you will work on unifying fragmented data pipelines, building robust data models, and ensuring reliable data lineage across multiple product areas.
not practised here - Streaming
-Build data platforms and frameworks that support both batch and real-time processing, enabling flexible, efficient analytics at scale.
- Cost & performance
-Evaluate and introduce emerging technologies to improve data platform performance, cost efficiency, and scalability.
- Failure handling
-Lead design and code reviews, driving engineering best practices across data development, documentation, testing, and monitoring.
- Spark
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
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
- 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
Streaming · Failure handling
- 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
Cost & performance · 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
Not covered by the plan: AWS, Governance & security.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 12 dremoved the moment Amazon closes it