Senior Data Engineer, Applied AI Solutions

Amazon is hiring a Senior Data Engineer, Applied AI Solutions in Seattle, United States. It pays $155k-$209k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this data engineer role is Works on AI ●●●○: the daily work is on AI products, without building the model.
The listed pay starts 5% above the typical data engineer posting on Level.
Tools and skills the posting cites include Bedrock, SageMaker and Amazon SageMaker.
Level classifies it as a senior role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

Design and build next-generation data infrastructure supporting both human analysts and AI systems and autonomous agents.

BedrockSageMakerAmazon SageMaker

ragai-agentsfine-tuningdata-engineeringvector-storesmachine-learning

The newest business group in AWS, Applied AI Solutions are built by AWS and AWS Partners to deliver applied AI solutions that leverage Amazon’s operational expertise and that businesses love and trust for their day-to-day success. Our ambition is to become a partner which companies can rely on to run their business every day, putting AI to work delivering better customer experience, operational excellence and speed.

We are seeking a Senior Data Engineer to design, build and maintain our next-generation data infrastructure - one that seamlessly serves both human analysts and AI systems. This role sits at the intersection of traditional enterprise data warehousing and innovative AI technologies, requiring someone who can bridge these worlds to create a unified, future-proof data ecosystem.

As a key member of our data team, you'll collaborate across organizational boundaries with data scientists, engineers, analytics teams, and business stakeholders to develop innovative and scalable solutions that push the boundaries of what's possible with our data assets.

You'll be responsible for ensuring our datasets maintain the highest levels of accuracy, consistency, and observability - implementing comprehensive monitoring, lineage tracking, and self-healing mechanisms that maintain data quality at scale. Your infrastructure will support both analysts / scientists and autonomous AI agents with equal effectiveness, requiring thoughtful interfaces, documentation, and metadata that serve both audiences.

In this role, you'll champion a forward-thinking approach to data infrastructure that anticipates the evolving needs of AI systems while maintaining the reliability and performance that business operations demand. You'll help shape our technical roadmap for data systems that will serve as the foundation for our organization's AI transformation journey.

Key job responsibilities
- 5+ years of data engineering, building and operating production pipelines and warehouses.
- Experience building data infrastructure that serves AI systems and autonomous agents, not just human analysts, including machine-consumable interfaces, metadata, and documentation.
- Experience with GenAI data patterns end to end: chunking, embeddings, and vector stores for retrieval-augmented generation.
- Experience building and maintaining datasets and feature pipelines for ML/GenAI training, fine-tuning, and inference (Amazon SageMaker, Bedrock, or equivalent).
- Experience implementing data quality, lineage, and observability that AI workloads depend on including validation, freshness/anomaly monitoring, and alerting at scale.
- 5+ years of Python (or Scala/Java) and advanced SQL, including performance tuning at scale.
- Experience with batch and streaming ETL/ELT on AWS (Glue, EMR/Spark, S3, Athena) and a production cloud data warehouse (Amazon Redshift or equivalent).
- Experience designing data models and schemas for analytical, operational, and AI/retrieval workloads.
- Experience with workflow orchestration (Step Functions, Airflow, or Glue Workflows).

Basic qualifications

- 7+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with SQL
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
- Experience mentoring team members on best practices
- Experience with MPP databases such as Amazon Redshift
- Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
- Experience building data infrastructure that serves AI systems and autonomous agents, not just human analysts, including machine-consumable interfaces, metadata, and documentation.
- Experience with GenAI data patterns end to end: chunking, embeddings, and vector stores for retrieval-augmented generation.
- Experience building and maintaining datasets and feature pipelines for ML/GenAI training, fine-tuning, and inference (Amazon SageMaker, Bedrock, or equivalent).
- Experience implementing data quality, lineage, and observability that AI workloads depend on including validation, freshness/anomaly monitoring, and alerting at scale.

Preferred qualifications

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience operating large data warehouses
- Experience providing technical leadership and mentoring other engineers for best practices on data engineering
- Bachelor's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent
- Knowledge of distributed systems as it pertains to data storage and computing

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 - 154,600.00 - 209,100.00 USD annually

How we rate this

Senior Data Engineer, Applied AI Solutions at Amazon rates 75 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

Senior Data Engineer, Applied AI Solutions is an open job at Amazon.
Level rates this role AI Level 3 of 4: Works on AI.
The listed salary is $155k-$209k a year.
The role is based in US, WA, Seattle.
The job was posted on September 24, 2026.
Seniority: senior.
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

RAGAI agentsFine-tuningData EngineeringVector StoresMachine learningBedrockSageMaker

Also common in data engineer jobs at this level

Machine learning Databricks Azure Snowflake Google Cloud LLM

Pay

This job starts at: $155k
Typical data engineer job: $147k

This posting starts 5% above the typical data engineer job. See data engineer jobs

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Tell me about a project where data engineering was part of your work. What did you do?
Tell me about a project where vector stores was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: RAG, AI agents, Fine-tuning, Data Engineering, and Vector Stores. 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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