Amazon · amazon.jobs · checked today
Data Engineer, Infra-Finance Business intelligence & Transformations
Do you have a desire to make a major contribution to the future in the rapid growth environment of Cloud Computing? Are you passionate about data and code? Does the prospect of dealing with massive volumes of data excite you?
Skills, with evidence
- Data modelling
Experience with data modeling, warehousing and building ETL pipelines
must have - Python
Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
must have - SQL
5+ years of 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 - 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
- Dashboards & BI
Own the design, development, and maintenance of ongoing metrics, reports, analyses, dashboards, etc.
not practised here - Java
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
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≈ 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≈ 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, Dashboards & BI, Java, Scala.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 7 dremoved the moment Amazon closes it