Data Engineer II, AWS Analytics Engineering

Amazon is hiring a Data Engineer II, AWS Analytics Engineering in Seattle, United States. It pays $132k-$179k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

Level rates this data engineer role Works on AI ●●●○: the daily work is on AI products, without building the model.
The posting mentions Bedrock and SageMaker among its tools and skills.
Level classes it as a mid-level role.
Amazon has 21,000+ other open roles on Level.
Posted in the last few days.

AI in this role

bedrocksagemaker

We are seeking a talented Data Engineer to join our EC2 + EBS + Networking Product BI team. You will own end-to-end analytics for one or more Compute service areas — building and monitoring ETL pipelines, delivering executive reports, creating metrics and callouts on service performance, and producing deep-dive analyses that shape product roadmaps for services used by millions of AWS customers worldwide.

Key job responsibilities
• ETL Monitoring & Pipeline Operations: Own the reliability and timeliness of 200+ data pipelines serving executive-facing reports. Today, the team uses automated alarms for real-time monitoring and AI-powered agents that self-heal failures and triage tasks — you will maintain and extend this system using Redshift, orchestration frameworks, and AWS data services.
• Report Delivery & Business Reviews: Own and operate Weekly/Daily Business Review (WBR/DBR) reporting for EC2 services, ensuring accurate, timely delivery to CEO/S-Team and VP-level stakeholders. Today, report generation, validation, and delivery are automated via LLMs — you will operate and improve these workflows.
• Callout Generation: Generate callouts on service performance — identifying week-over-week trends, anomalies, and key drivers to surface in executive reviews. Today, callout generation across all 9 services is LLM-automated — you will own the quality bar and continuously improve prompt and pipeline accuracy.
• Data Ingestion & Metric Creation: Ingest new data sources and build metrics that track business health (usage, capacity, adoption, customer growth). Today, the team maintains scalable ETL pipelines in Redshift with workflow orchestration — you will ensure metrics remain sustainable, reproducible, and available on demand.
• Analysis & Deep Dives: Conduct deep-dive analyses for Product Managers, Sales, and Finance teams. Today, the team uses a proprietary analytics platform with governed SOPs, stored context, and pre-built queries — enabling any stakeholder to deep-dive with technical depth and reproducibility. You will author and maintain these SOPs and analyses.
• Dashboard Development: Design, build, and maintain interactive dashboards (QuickSight, Weave) covering product trends, customer growth, instance utilization, and feature adoption. Today, dashboards serve 100+ Product Managers across 11 engagement areas — you will own dashboards for your service areas and improve self-serve capabilities.
• Stakeholder Partnership: Work closely with Product Managers, service finance teams, and cross-functional partners to translate business questions into data products and present findings to senior leaders. Today, the team partners across EC2, Networking, and Compute product families — you will be the primary analytics point of contact for your assigned service areas.

About the team
We are the EC2 + Networking Product BI team within AWS Analytics & Engineering (AAE). We serve as the data and analytics partner for Amazon Elastic Compute Cloud (EC2), Networking Services, and adjacent Compute product families — supporting 100+ Product Managers across 11 engagement areas.
Our core work includes:
• ETL Pipeline Monitoring & Operations: We build and operate 200+ data pipelines that ingest, transform, and serve petabyte-scale datasets across EC2, Networking, and Compute services. Reliability and timeliness are critical — our reports feed directly into CEO/S-Team weekly reviews. We use automated alarms for real-time pipeline monitoring and AI-powered agents to self-heal failures and triage tasks, minimizing manual intervention at this scale.
• Report Delivery & Callout Generation: We deliver Weekly and Daily Business Review (WBR/DBR) reports across 9 Compute services (EC2 Core, Gen AI, EBS, Bedrock, SageMaker, ECS, EKS, ECR, Lambda), generating callouts on service performance that inform executive decision-making. Report generation, validation, and callout generation are all done via LLMs — enabling consistent quality, accuracy, and speed at scale.
• Data Ingestion & Metric Creation: We design and maintain metrics that track business health — usage, capacity, adoption, customer growth — ensuring they are sustainable, reproducible, and available on demand.
• Analysis & Deep Dives: We produce analyses for Product Managers, Sales, and Finance teams using our proprietary analytics platform where governed SOPs, context, and prior work live. These SOPs help all PMs, Sales, and Finance stakeholders deep-dive with governed data, technical depth, and the right queries — enabling reproducibility and easy follow-ups across the organization.
Everything we build is designed to be sustainable, reproducible, and available on demand — so any team member can pick up, extend, or re-run work without starting from scratch.

Basic qualifications

- 3+ years of data engineering experience
- 3+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
- 3+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies experience
- 3+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
- 3+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience
- 3+ years of developing and operating large-scale data structures for business intelligence analytics using Oracle experience
- 3+ years of in the job offered or a related occupation experience
- 1+ years of developing and operating large-scale data structures for business intelligence analytics using each of the following: ETL (Extract, Transform, Load)/ELT (Extract, Load, Transform) processes experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field
- 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)

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 132,100.00 - 178,800.00 USD annually

How we rate this

Data Engineer II, AWS Analytics Engineering at Amazon rates 64 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.

Key facts

Data Engineer II, AWS Analytics Engineering is an open job at Amazon.
Level rates this role AI Level 3 of 4: Works on AI.
The listed salary is $132k-$179k a year.
The role is based in US, WA, Seattle.
The job was posted on October 29, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

BedrockSageMaker

Also common in data engineer jobs at this level

Python Spark Machine learning Airflow Databricks Azure

Pay

This job starts at: $132k
Typical data engineer job: $150k

This posting starts 12% below the typical data engineer job. See data engineer jobs

Questions you could be asked

What's a project where you used Bedrock hands-on?
Walk me through how you've used SageMaker in your day-to-day work.
Describe a typical day in a role like this one: which parts run through AI directly?
If you removed AI from this role, what would be left, and how do you decide what still needs a human?

Adapt your resume

List these exact terms on your resume: Bedrock and SageMaker. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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