Amazon · amazon.jobs · checked today
Data Engineer, Advertising Demand Enablement & Product Ops (DEPO)
Are you passionate about building data infrastructure that delivers real business value at massive scale? As a Data Engineer at Amazon, you will design, build, and optimize data pipelines and architectures that transform raw data into actionable insights. You will work alongside software engineers, data scientists, analysts, and business stakeholders to create reliable, performant data solutions that power critical decision-making.
Skills, with evidence
- Data modelling
Create coherent logical and physical data models that accurately represent business requirements, ensure data integrity and consistency, and drive efficient storage and retrieval across relational and distributed systems.
must have - Data quality
applying best practices in data quality, lineage tracking, and pipeline monitoring to ensure reliable and performant data delivery.
must have - SQL
Develop and optimize SQL queries and scripts to extract, transform, and manipulate large-scale datasets, leveraging distributed data processing frameworks to handle high-volume workloads efficiently.
must have - Streaming
Knowledge of batch and streaming data architectures like Kafka, Kinesis, Flink, Storm, Beam.
must have - AWS
Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets.
not practised here - Cost & performance
As a Data Engineer at Amazon, you will design, build, and optimize data pipelines and architectures that transform raw data into actionable insights.
- Failure handling
- Design, build, and maintain scalable ETL/ELT pipelines and data infrastructure, applying best practices in data quality, lineage tracking, and pipeline monitoring to ensure reliable and performant data delivery.
- Spark
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Governance & security
- Design, build, and maintain scalable ETL/ELT pipelines and data infrastructure, applying best practices in data quality, lineage tracking, and pipeline monitoring to ensure reliable and performant data delivery.
not practised here - Machine learning
You will start your day reviewing pipeline health and data quality metrics, then shift into building or refining ETL workflows that serve downstream analytics and machine learning use cases.
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
Streaming · 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
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
20 drills · Foundations + Intermediate≈ 8 hours
Not covered by the plan: AWS, Governance & security, Machine learning.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 9 dremoved the moment Amazon closes it