Data Engineer, Amazon Security Experience

Amazon is hiring a Data Engineer, Amazon Security Experience in Seattle, United States. It pays $101k-$160k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

Level rates this data engineer role Little AI ●○○○: aI is not part of the work.
Tools and skills the posting cites include data engineering, etl and sql.
Level classifies it as a junior role.
Level lists 21,000+ other open roles at Amazon.
The role was posted within the past two weeks.

AI in this role

Build and maintain secure data pipelines and analytical models for Amazon Security experiences.

data-engineeringetlsqldata-modelingpipeline-optimization

The Amazon Security Experiences (ASE) organization is the data intelligence backbone of Amazon Security. We build and operate the data platform that powers security insights, builder experience analytics, and tooling decisions across Amazon's security ecosystem. Every insight surfaced to security leadership, from builder productivity trends to security issue quality metrics, flows through systems our team designs, builds, and maintains.

We operate at scale, processing large volumes of security and operational data daily through hundreds of pipelines consisting of transformations, reporting queries, ingestions, and infrastructure management scripts. Our engineers work directly with source systems across AWS and SDO (Security, Developer, and Operations) data domains to procure data, convert it into structured formats, build large-scale processing pipelines, design analytical data models, and maintain infrastructure with the highest security and compliance standards.

We are seeking a Data Engineer I to join our Security Data Intelligence (SDI) team. In this role, you will build and maintain data pipelines that power security analytics across Amazon. Working with well-defined requirements and guidance from senior engineers, you will create physical data models, optimize data pipelines for defined datasets, and deliver reliable, well-documented solutions on schedule. You will partner with teams across ASE, BizOps, MapSec, and other security organizations to implement data ingestion flows and support analytical models that serve the broader Amazon Security organization.

Key job responsibilities
Learn and apply team-established data processing guidelines and best practices when building and testing pipelines. Measure and improve dataset quality across owned pipelines, identifying issues and proposing solutions to the team. Flag limitations or opportunities in data processing tools to senior engineers for evaluation.

Learn and understand the team's existing data architecture, including how security data flows through ingestion, transformation, and serving layers. Build data solutions at the component level within the established team data architecture, following defined logical data models. Create physical data models for assigned datasets, applying appropriate persistence and integration techniques. Understand the implications of implementation decisions (e.g., normalization, distribution) and seek guidance from senior engineers on trade-offs.

Write secure, stable, testable, and maintainable code with minimal defects, following team coding standards. Clearly document solutions for ease of use and maintainability by teammates and data consumers. Get all data models, pipeline designs, and code reviewed by senior engineers before deployment. Test code thoroughly and understand the maintenance characteristics, runtime properties, and dependencies of delivered solutions.

Build and maintain data pipeline infrastructure for assigned datasets using team-established patterns and tools. Apply basic database optimization techniques such as partitioning, distribution, and indexing as directed. Understand the underlying system infrastructure that data solutions depend on, including differences between data persistence solutions. Follow team guidelines for infrastructure management, security compliance, and automation practices.

Solve straightforward, well-defined problems with guidance from senior engineers, for example, building ingestion pipelines for a new security data source where the schema and requirements are defined, or troubleshooting data quality issues in existing datasets by researching root causes and thoroughly resolving defects. Handle problems even outside your own domain by either resolving them or ensuring a clear hand-off to the right owner.

Take well-defined requirements and deliver solutions on schedule within assigned scope. Participate in team design, scoping, and prioritization discussions to understand project context. Manage your own task execution effectively, seeking guidance when blocked or uncertain. Communicate progress and dependencies clearly to your team lead and senior engineers.

Build good working relationships with teammates, peer engineers, and data consumers across ASE and SDO. Partner effectively with Business Analysts, Data Scientists, and peer SDEs to understand data requirements. Help train and mentor DE interns when applicable. Participate in the interview process. Recognize discordant views and participate in constructive dialogue with peers.

Follow team-established best practices for data discovery, naming conventions, and data security. Classify, store, and handle data per Amazon security and compliance policies. Ensure proper access control, data integrity, and auditability for all produced datasets. Track security risks in data handling, mitigate issues, and escalate in a timely manner.

Identify data quality issues and pipeline deficiencies in owned components and propose solutions to the team. Participate in code reviews, design discussions, and team planning sessions. Make small improvements to team development processes when directed. Actively seek knowledge of team data architecture and apply learnings to improve your data solutions.

Participate in on-call rotation and respond to pipeline incidents following established runbooks and escalation procedures. Understand the maintenance characteristics, runtime properties, and dependencies of team data systems. Contribute to monitoring and alarming setup for owned pipelines with guidance from senior engineers. Document incident resolutions and contribute to runbook updates for owned components.

Basic qualifications

- 2+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)

Preferred qualifications

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.

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 - 101,300.00 - 160,000.00 USD annually

How we rate this

Data Engineer, Amazon Security Experience at Amazon rates 0 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

●●●● 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, Amazon Security Experience is an open job at Amazon.
Level rates this role AI Level 1 of 4: Little AI.
The listed salary is $101k-$160k a year.
The role is based in US, WA, Seattle.
The job was posted on October 19, 2026.
Seniority: junior.
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

Data EngineeringETLSQLData ModelingPipeline Optimization

Also common in data engineer jobs at this level

Azure Airflow Databricks Google Cloud Snowflake Java

Pay

This job starts at: $101k
Typical data engineer job: $150k

This posting starts 32% below the typical data engineer job. See data engineer jobs

Questions you could be asked

Tell me about a project where data engineering was part of your work. What did you do?
Tell me about a project where etl was part of your work. What did you do?
Tell me about a project where sql was part of your work. What did you do?
Tell me about a project where data modeling was part of your work. What did you do?
Tell me about a project where pipeline optimization was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Data Engineering, ETL, SQL, Data Modeling, and Pipeline Optimization. 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.

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