Data Engineer II, Alexa AI - ML Engineering

Amazon is hiring a Data Engineer II, Alexa AI - ML Engineering in Bengaluru, India. Level rates it ; you can apply on Amazon.

Level's read on this job

Works on AI ●●●○ is how Level rates this machine learning engineer role: the daily work is on AI products, without building the model.
Level classifies it as a mid-level role.
Level lists 20,000+ other open roles at Amazon.
The role was posted in the last few days.

AI in this role

Join Alexa AI - ML Engineering and help power personalized, proactive experiences for hundreds of millions of Alexa customers worldwide. As a Data Engineer II, you will design and own the large-scale data pipelines that turn raw customer-interaction signals into the context and features our personalization and proactive-agent models depend on, working side-by-side with SDEs to shape our data strategy and architecture.

- 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.
- You will partner with SDEs and applied scientists to define data strategy, contracts, and feature pipelines, and to decide architecture direction together.
- You will own operational health: data migrations, schema/platform updates, and triage of pipeline tickets, directly supporting the SDE team's work.
- This is a high-visibility role: the data you deliver, and keep running, directly shapes what Alexa proactively tells customers, and when.

Key job responsibilities
1. Partner closely with SDEs to help define data strategy, architecture direction, and the long-term roadmap for the team's data platform.
2. Design, build, and own ETL/ELT pipelines that ingest, transform, and load customer-interaction and context data at a scale of billions of records per day.
3. Own operational items end-to-end: data migrations, schema and platform updates, and triage/resolution of data-pipeline tickets, providing day-to-day support to the SDE team.
4. Partner with applied scientists and ML engineers to define data contracts, feature pipelines, and data-quality checks feeding personalization and proactive-agent models.
5. Operate and continuously improve production pipelines: optimize data delivery, redesign for scalability, manage deployments, and drive root-cause fixes for pipeline incidents.
6. Establish and document best practices for data modeling, warehousing, and pipeline design across the team.

A day in the life
You'll join SDEs in a design discussion to shape data strategy and architecture, then pick up a migration or platform-update ticket and see it through to resolution. You'll trace a data-quality issue in a personalization feature pipeline, support SDEs with any operational asks on your pipelines, and monitor the health of systems processing billions of daily customer-interaction events. You'll partner with applied scientists to onboard new signal sources and triage pipeline alerts.

About the team
Alexa AI - ML Engineering builds the personalization and proactive-intelligence systems that let Alexa anticipate what customers need before they ask. Data Engineers work side-by-side with SDEs and applied scientists, combining data strategy and large-scale engineering to turn billions of daily customer-interaction signals into the context, features, and content that drive Alexa's ranking, personalization, and proactive-agent experiences. We value ownership, data quality, and working backwards from the customer.

Basic qualifications

- 3+ years of data engineering experience
- 4+ years of SQL experience
- Experience with data modeling, warehousing and building ETL pipelines

Preferred qualifications

- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

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How we rate this

Data Engineer II, Alexa AI - ML Engineering at Amazon rates 68 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.