Amazon · amazon.jobs · checked today
AI Platform Data Engineer, Ring Agent Platform Org
We are seeking a Platform Builder—a Data Engineer focused on developing platforms and scalable data solutions—with strong technical, analytical, communication, and stakeholder management skills. This role sits at the intersection of data engineering, business intelligence, and platform engineering—requiring partnership with software development engineers, scientists, data analysts, and business stakeholders across subscriptions and monitoring services. You will design, implement, and maintain platform features and curated datasets that power subscription lifecycle analytics, monitoring service
Skills, with evidence
- Data modelling
Experience with data modeling, warehousing and building ETL pipelines
must have - SQL
Experience with SQL
must have - AWS
Knowledge of AWS Infrastructure
must have · not practised here - Failure handling
This role sits at the intersection of data engineering, business intelligence, and platform engineering—requiring partnership with software development engineers, scientists, data analysts, and business stakeholders across subscriptions and monitoring services.
- Governance & security
* Implement data governance components including data classification, PII detection, and lineage tracking
not practised here - Cost & performance
This role requires a a thoughtful, fundamentals-driven approach to building robust, observable data infrastructure—from designing efficient pipelines and event-driven architectures, to leveraging automation (including AI-assisted tooling) to optimize code and workflows, to creating platforms that surface actionable insights on business health and service reliability.
- Data quality
You will build scalable infrastructure, automate repetitive processes, and create systems that continuously enhance data quality, discoverability, and usability across our subscription and monitoring ecosystem.
- Streaming
This role requires a a thoughtful, fundamentals-driven approach to building robust, observable data infrastructure—from designing efficient pipelines and event-driven architectures, to leveraging automation (including AI-assisted tooling) to optimize code and workflows, to creating platforms that surface actionable insights on business health and service reliability.
- Dashboards & BI
- Knowledge of BI analytics, reporting or visualization tools like Tableau, AWS QuickSight, Cognos or other third-party tools
not practised here - Scala
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
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
- 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≈ 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, Governance & security, Dashboards & BI, Scala.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 12 dremoved the moment Amazon closes it