Amazon · amazon.jobs · checked today
Data Engineer, Marketing Tech BI, Stores Finance Analytics & Insights
Mkt Tech BI team owns one of the largest datasets at Amazon, our team has various backgrounds that provide you the opportunity to learn each other. Our ultimate goal is to build a robust/scalable data infrastructure and automated reporting system to empower users have maximum flexibility to play with the data in order to maximize Long Term Free Cash Flow(LTFCF). The ideal candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and, above all else, is passionate about data and analytics.
Skills, with evidence
- Data modelling
1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
must have - Data quality
Implement data validation, lineage, documentation, and operational mechanisms that improve trust and reduce single points of failure
must have - Python
Build and maintain scalable ETL/ELT workflows using SQL, Python, AWS services, and big data technologies
must have - SQL
Build and maintain scalable ETL/ELT workflows using SQL, Python, AWS services, and big data technologies
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
Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
must have · not practised here - Failure handling
- Operate and improve data infrastructure, including Redshift clusters, data lake tables, orchestration workflows, monitoring, alerting, and data quality controls
- Governance & security
- Partner with Data Science, Business Intelligence, Product, Finance, Engineering, Privacy, and Legal stakeholders to translate business and compliance requirements into scalable data solutions
not practised here - Airflow / orchestration
- Operate and improve data infrastructure, including Redshift clusters, data lake tables, orchestration workflows, monitoring, alerting, and data quality controls
- Streaming
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Scala
- Experience with one or more scripting language (e.g., Python, KornShell, Scala)
not practised here
Your plan
- The SQL screen: correct, then fast≈ 1 h
Data quality · SQL
- 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
- 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≈ 3 h
Failure handling · Airflow / orchestration · Streaming
- After the first one finishesFoundations
- Run it for last TuesdayFoundations
- The history nobody keptFoundations
- The spreadsheet is a dependencyFoundations
- Too slow by morningFoundations
- 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
Not covered by the plan: AWS, Governance & security, Scala.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 8 dremoved the moment Amazon closes it