Amazon · amazon.jobs · checked today
Data Engineer II, SPIA
Join Amazon’s Selling Partner Insights and Analytics (SPIA) organization as a Data Engineer, where you'll build the data infrastructure powering 200+ business teams across Amazon Stores. Shape the future of data engineering for a platform processing millions of cases daily, driving insights that directly impact customer experience and operational excellence across the world's largest e-commerce ecosystem. Paragon is a case management system (CMS) used by 180+ business teams within Stores to manage their communications and interactions with their customers.
Skills, with evidence
- Data modelling
Experience with data modeling, warehousing and building ETL pipelines
must have - SQL
Experience with SQL
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
Build solutions to achieve BAA(Best At Amazon) standards for system efficiency, IMR efficiency, data availability, consistency & compliance.
not practised here - Cost & performance
Design and implement scalable and cost effective data infrastructure to enable Non-IN(Emerging Marketplaces and WW) use cases on our data platform
- Spark
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Streaming
- 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
- 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
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 3 dremoved the moment Amazon closes it