Amazon · amazon.jobs · checked today
Data Engineer , Amazon Customer Service
Customer Experience Products (CXP) is part of Amazon's Customer Service (CS) organization, responsible for the data infrastructure that powers measurement, analytics, and automation across every customer service interaction — chat, voice, bots, and digital self-service. Our Data Engineers own the foundational data layer that BIEs, scientists, and product teams rely on to evaluate performance, shape OP planning, and drive customer-facing product improvements. You will join a team of data engineers, BIEs, and analysts who build and maintain the pipelines, schemas, and data platforms that underpi
Skills, with evidence
- Data modelling
Design and implement logical and physical data models for complex, large-scale datasets that drive downstream analytics
must have - Data quality
Own data quality end-to-end. Establish SLAs, define data certification standards, build monitoring and alerting for pipeline health, and proactively identify and resolve data quality gaps
must have - SQL
1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience
must have - Spark
help optimize a Spark job that's approaching its SLA window
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 - Cost & performance
- Build and optimize data pipelines (ETL/ELT) for difficult and large-scale datasets using technologies such as AWS Glue, Spark, Redshift, and EMR.
- Failure handling
Establish SLAs, define data certification standards, build monitoring and alerting for pipeline health, and proactively identify and resolve data quality gaps (e.g., upstream DQ issues in Panorama tables, source data discrepancies).
- Streaming
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Dashboards & BI
Own pipeline reliability for business-critical reporting surfaces including VP-level dashboards and weekly business review decks.
not practised here - Governance & security
Build tools and processes for data lineage tracking, discoverability, and governance.
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
- 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≈ 3 h
Failure handling · Streaming
- 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, Dashboards & BI, Governance & security.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 6 dremoved the moment Amazon closes it