Amazon · amazon.jobs · checked today
Data Engineer II, Alexa AI - ML Engineering
Skills, with evidence
- Data modelling
Experience with data modeling, warehousing and building ETL pipelines
must have - SQL
4+ years of SQL experience
must have - Warehousing
Experience with data modeling, warehousing and building ETL pipelines
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 - Spark
- You will build and operate ETL/ELT pipelines processing billions of records a day, using AWS big-data services (Glue, EMR, Kinesis, Redshift, DynamoDB) and distributed compute.
- Streaming
- You will build and operate ETL/ELT pipelines processing billions of records a day, using AWS big-data services (Glue, EMR, Kinesis, Redshift, DynamoDB) and distributed compute.
- Cost & performance
Operate and continuously improve production pipelines: optimize data delivery, redesign for scalability, manage deployments, and drive root-cause fixes for pipeline incidents.
- Data quality
We value ownership, data quality, and working backwards from the customer.
- Failure handling
Operate and continuously improve production pipelines: optimize data delivery, redesign for scalability, manage deployments, and drive root-cause fixes for pipeline incidents.
- Schema evolution & contracts
Partner with applied scientists and ML engineers to define data contracts, feature pipelines, and data-quality checks feeding personalization and proactive-agent models.
Your plan
- SQL
drills at the Mid level
- Modelling
drills at the Mid level
- Pipelines
drills at the Mid level
- Spark
drills at the Mid level
The full plan, drill by drill, is on the job page.
leaves in 12 dremoved the moment Amazon closes it
