Senior Data Engineer, Amazon Security Experience
Amazon · Austin, Texas, USA ·
- Seniority
- Senior
- Category
- Data engineering
Senior Data Engineer owning large-scale data pipelines and analytical models for Amazon Security's internal data platform: building ingestion, transformation, and reporting pipelines across AWS and internal security data domains, defining data architecture, and driving reliability, monitoring, and on-call health of hundreds of pipelines.
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 Senior Data Engineer to join our Security Data Intelligence (SDI) team. This individual will own multiple data domains end-to-end, operate hundreds of pipelines, and drive architectural improvements that impact how security leadership makes decisions. You will partner with teams across ASE, BizOps, MapSec, and other security organizations to design data contracts, build ingestion flows, and deliver analytical models that serve the broader Amazon Security organization
Key job responsibilities
Identify limitations and opportunities in data processing tools, drive improvements and innovation, define data processing guidelines, and ensure best practices in all pipelines designed and reviewed. For example: redesigning ingestion frameworks to handle new security telemetry data, or building reusable transformation patterns adopted across multiple teams.
Define and own data architecture at the team and organization level, ensuring architecture effectively matches business problems and data challenges with security, scalability, and cost effectiveness. Show good judgment making technical trade-offs between short-term technology needs and long-term business needs.
Produce exemplary code, solutions that are easily usable by customers, inventive, secure, easily maintainable, appropriately scalable, and extensible. Build solutions that are easy for others to contribute to. Work to simplify, optimize, and remove bottlenecks.
Define and own infrastructure architecture at the team level. Anticipate data management and access patterns, evolve the technology stack to remove bottlenecks, and deliver systems that are secure, scalable, and long lasting. Define team-level guidelines and best practices for infrastructure management and automation.
Solve complex ambiguous problems, for example, designing cross-domain data models that unify AWS and SDO data where definitions, schemas, and ownership vary across multiple security programs, or combining multiple datasets to solve problems that couldn't be solved before. Spot areas that might lead to customer confusion, data misinterpretation, or gaps in data contracts.
Effectively split project work into parallel tasks that can be performed by themselves and others and reassembled successfully. Drive to completion projects with dependencies on peers or other teams.
Influence related teams' data architecture and software design. Provide technical assessments for promotions. Actively mentor and develop others. Build consensus when confronted with discordant views.
Drive data engineering best practices, Data Discovery, Naming Conventions, Operational Excellence, Data Security. Ensure team's data is auditable, available, and accessible.
Proactively fix data architecture deficiencies and propose larger projects which may require the work of other teams. Drive improvements through code review, design discussions, team planning, and operational reviews.
Participate in on-call rotation and own operational health of data systems, establish monitoring, alarming, runbooks, and SLA tracking. Drive continuous improvement in reliability and incident response.
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 Senior Data Engineer to join our Security Data Intelligence (SDI) team. This individual will own multiple data domains end-to-end, operate hundreds of pipelines, and drive architectural improvements that impact how security leadership makes decisions. You will partner with teams across ASE, BizOps, MapSec, and other security organizations to design data contracts, build ingestion flows, and deliver analytical models that serve the broader Amazon Security organization
Key job responsibilities
Identify limitations and opportunities in data processing tools, drive improvements and innovation, define data processing guidelines, and ensure best practices in all pipelines designed and reviewed. For example: redesigning ingestion frameworks to handle new security telemetry data, or building reusable transformation patterns adopted across multiple teams.
Define and own data architecture at the team and organization level, ensuring architecture effectively matches business problems and data challenges with security, scalability, and cost effectiveness. Show good judgment making technical trade-offs between short-term technology needs and long-term business needs.
Produce exemplary code, solutions that are easily usable by customers, inventive, secure, easily maintainable, appropriately scalable, and extensible. Build solutions that are easy for others to contribute to. Work to simplify, optimize, and remove bottlenecks.
Define and own infrastructure architecture at the team level. Anticipate data management and access patterns, evolve the technology stack to remove bottlenecks, and deliver systems that are secure, scalable, and long lasting. Define team-level guidelines and best practices for infrastructure management and automation.
Solve complex ambiguous problems, for example, designing cross-domain data models that unify AWS and SDO data where definitions, schemas, and ownership vary across multiple security programs, or combining multiple datasets to solve problems that couldn't be solved before. Spot areas that might lead to customer confusion, data misinterpretation, or gaps in data contracts.
Effectively split project work into parallel tasks that can be performed by themselves and others and reassembled successfully. Drive to completion projects with dependencies on peers or other teams.
Influence related teams' data architecture and software design. Provide technical assessments for promotions. Actively mentor and develop others. Build consensus when confronted with discordant views.
Drive data engineering best practices, Data Discovery, Naming Conventions, Operational Excellence, Data Security. Ensure team's data is auditable, available, and accessible.
Proactively fix data architecture deficiencies and propose larger projects which may require the work of other teams. Drive improvements through code review, design discussions, team planning, and operational reviews.
Participate in on-call rotation and own operational health of data systems, establish monitoring, alarming, runbooks, and SLA tracking. Drive continuous improvement in reliability and incident response.