Amazon · amazon.jobs · checked today
Software Development Engineer, AWS Analytics Engineering
We are seeking a Software Development Engineer to join our team and drive the next generation of our data platform and AI powered products at AWS scale. You will own end to end design, implementation, and operation of distributed systems that process petabyte scale data daily, serve hundreds of internal customers, and directly influence how AWS leadership makes decisions. This is a high impact role where you will shape the architecture of both products, building workflow orchestration systems, multi agent AI frameworks, GenAI powered analytics capabilities, and scalable platform services.
Skills, with evidence
- AWS
You will design and build distributed systems that orchestrate thousands of daily workflows and process petabyte scale data across AWS services.
must have · not practised here - Java
1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience.
must have · not practised here - Machine learning
Innovate with AI/ML technologies. Stay current with GenAI, LLM, and ML advancements.
must have · not practised here - Data modelling
You will own end to end design, implementation, and operation of distributed systems that process petabyte scale data daily.
must have - Data quality
Drive operational excellence. Own the operational health of production systems.
must have - Airflow / orchestration
This is a high impact role where you will shape the architecture of both products, building workflow orchestration systems, multi agent AI frameworks, GenAI powered analytics capabilities, and scalable platform services.
- Cost & performance
Design and implement scalable, secure, and cost effective distributed systems for workflow orchestration and AI powered analytics.
- Failure handling
Examples include building new marketplace and IDE experiences, implementing validated query patterns, designing cross service governed metrics, or creating real time monitoring dashboards.
- Streaming
Examples include building new marketplace and IDE experiences, implementing validated query patterns, designing cross service governed metrics, or creating real time monitoring dashboards.
- Dashboards & BI
Examples include building new marketplace and IDE experiences, implementing validated query patterns, designing cross service governed metrics, or creating real time monitoring dashboards.
not practised here
Your plan
- The SQL screen: correct, then fast≈ 1 h
Data quality
- Average order value by countryFoundations
- Bucket missing countriesFoundations
- Customers who never orderedFoundations
- Hiring funnel by roleFoundations
- Orders with no line itemsFoundations
- 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
Airflow / orchestration · Failure handling · 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≈ 2 h
Cost & performance
- HAVING vs WHERE: where does a filter after groupBy actually run?Foundations
- partitionBy("country"): what does partition pruning look like in the plan?Foundations
- Predicate pushdown: equality, ranges, and the filter that cannot be pushedFoundations
- Revenue by country: read the DAG behind one groupByFoundations
- Too many small files: where do they cost you, and how does compaction help?Foundations
- Say it out loud≈ 1 h
Not covered by the plan: AWS, Java, Machine learning, Dashboards & BI.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 8 dremoved the moment Amazon closes it