Amazon · amazon.jobs · checked today
Sr. Data & Analytics Engineer, Amazon Leo
Amazon Leo is Amazon's low Earth orbit satellite network. Our mission is to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon Leo will serve people and organizations operating in locations without reliable connectivity.
Skills, with evidence
- Data modelling
Build unified data models connecting cells, PCBAs, battery packs, test results, manufacturing processes, equipment, configurations, and quality records.
must have - Data quality
Establish data quality, lineage, metadata, and schema standards to ensure engineering data is accurate, reproducible, and trustworthy.
must have - Python
Strong programming experience with Python or another modern programming language and strong SQL skills.
must have - SQL
Strong programming experience with Python or another modern programming language and strong SQL skills.
must have - AWS
Experience with AWS data technologies, APIs, streaming architectures, data lineage, metadata management, or schema versioning.
not practised here - Airflow / orchestration
Develop automated data ingestion, transformation, validation, and processing from test systems, manufacturing equipment, databases, APIs, and engineering files.
- Failure handling
You will enable end-to-end traceability, automate data processing and reporting, and develop scalable tools that help engineers understand product performance, identify failures and trends, improve processes, and make faster technical decisions.
- Dashboards & BI
- Build engineering analytics, dashboards, and tools for test performance, lifecycle data, FPY, SPC, production trends, equipment performance, and failure investigations.
not practised here - Governance & security
- Establish data quality, lineage, metadata, and schema standards to ensure engineering data is accurate, reproducible, and trustworthy.
not practised here - Streaming
- Experience with AWS data technologies, APIs, streaming architectures, data lineage, metadata management, or schema versioning.
Your plan
- The SQL screen: correct, then fast≈ 2 h
Data quality · SQL
- Median delivery time per cityIntermediate
- Bucket deliveries into quartilesIntermediate
- Median order value without a median functionIntermediate
- New and repeat orders by monthIntermediate
- Every order against its customer's averageIntermediate
- Python: the data-wrangling round≈ 3 h
Python
- Diff two snapshots of a tableIntermediate
- Explode an array column into rowsIntermediate
- Flatten nested event payloadsIntermediate
- Pivot a long metrics table to wideIntermediate
- Choose what an incremental run should readIntermediate
- Data modelling: the round most people fail≈ 3 h
Data modelling
- Addresses that stay true to the pastIntermediate
- Seat holds and the release-night raceIntermediate
- Subscription warehouse grainIntermediate
- Campaign efficiencyIntermediate
- Catalogue: products, variants and sellersIntermediate
- Pipeline design: safe to run twice≈ 4 h
Airflow / orchestration · Failure handling · Streaming
- The source will not let youIntermediate
- Parcel tracking pipelineIntermediate
- Is this change safe?Intermediate
- Marketplace transactions at scaleIntermediate
- Five minutes behind the sourceIntermediate
- Say it out loud≈ 1 h
20 drills · Intermediate + Advanced≈ 12 hours
Not covered by the plan: AWS, Dashboards & BI, Governance & security.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 7 dremoved the moment Amazon closes it