Amazon · amazon.jobs · checked today
Data Engineer, Deal Intelligence & Automation, AWS GDSP
The Global Deal Strategy and Programs (GDSP) organization is seeking a Data Engineer to own the data infrastructure that unlocks deal intelligence and automation capabilities across the organization. The role is responsible for designing, building, and maintaining the pipelines, data models, and platforms that enable deal teams to access precise, reliable insights from broad data sets (deal telemetry, pricing models, customer usage, pipeline signals) at scale, with speed and accuracy that hold as volume grows. These data products serve deal strategists, pricing leaders, and senior executives w
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
Experience with SQL
must have - Spark
Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
must have - Warehousing
Experience with data modeling, warehousing and building ETL pipelines
must have - AWS
Leverage AWS services and generative AI to build next-generation data solutions that improve efficiency and unlock new analytical capabilities
must have · not practised here - Cost & performance
- Automate and optimize reporting processes to enable self-service analytics at scale, reducing manual effort and improving speed to insight
- Data quality
- Ensure data quality through monitoring, validation, auditing, and documentation of pipelines and data sources
- Failure handling
- Ensure data quality through monitoring, validation, auditing, and documentation of pipelines and data sources
- 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 · 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 · 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
- 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 · Cost & performance
- 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, Java, Scala.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 12 dremoved the moment Amazon closes it