Computer engineering Jobs in Seattle, WA

The AWS Analytics Engineering (AAE) organization is the analytics backbone of AWS. We build and operate the data systems that drive business decisions across more than 150 AWS services. Every insight that reaches AWS product leadership, from service adoption trends to revenue drivers, flows through systems our team designs, builds and maintains.

We work at very large scale. We run petabyte-scale data engineering across thousands of daily data jobs, hundreds of data models and thousands of curated datasets. We own the full data lifecycle, from raw ingestion to analytics ready for executives.

We are seeking a Senior Manager to lead a teams of data engineers. You will own the data solutions your team builds from start to finish: the pipelines, data models and curated datasets, plus the quality, lineage and compliance standards that make them trustworthy. You will report directly to the Director of Analytics Engineering.

In this role, you will build and lead a high-performing team of data engineers . You will set the technical vision and delivery roadmap, drive operational excellence (including on-call and operational ownership), and own hiring to grow the team as AAE's demand grows. You are accountable for the data your team produces. That means making sure its datasets, models and schemas are accurate, timely, well governed and trusted by the people who use them.

You will partner closely with BI, Applied Science and SDE teams. You will also represent your team to stakeholders across AWS, including product, sales and finance leadership. Your customers are the BI engineers, analysts and data scientists who rely on your team's data every day to deliver insights to AWS VP/SVP leadership.

The ideal candidate is a technical leader who has managed data engineering teams at scale and has deep hands-on data engineering experience. You know how to balance strategy with urgent delivery. You care about developing engineers, setting high bars and building data solutions that serve many internal customers. You will work with a lot of ambiguity: defining team scope, making build-vs-buy decisions, and prioritizing across competing demands from multiple VP-level stakeholders.

Key job responsibilities
Build, lead and develop a high-performing data engineering team . Own hiring, onboarding, performance management, promotions and career growth, and raise the technical bar across the team.
Set the team's technical vision and delivery roadmap with senior and principal engineers. Turn long-term goals into quarterly plans with clear milestones and owners.
Own delivery of the team's data solutions from start to finish: pipelines, data models, curated datasets, automation and operational tooling, across 3–5 data domains and hundreds of pipelines.
Own data quality. Make sure the team's models, schemas and datasets are accurate, complete, well documented and trusted. Build automated checks for validation, freshness and anomaly detection.
Treat governance and compliance as core work: lineage, metadata, access controls, data classification, retention and audit readiness for every data asset the team owns.
Drive operational excellence. Own the team's on-call rotation, SLAs, incident response, runbooks and reliability metrics, and keep improving them.
Work with the Director on priorities, resource allocation and headcount planning. Give regular updates on status, risks and delivery forecasts.
Work closely with BI, Applied Science and SDE teams and with business stakeholders in product, sales and finance, so the team's work matches business needs.
Lead modernization of the team's architecture, tools and practices, including adopting AI/ML-assisted data engineering where it adds value.
Set and enforce engineering best practices: code reviews, testing, documentation, security and cost discipline.
Manage dependencies across teams. Unblock engineers, raise risks early and make sure cross-team projects land on schedule.
Represent the team in business and technical reviews (WBRs, DBRs, OP1/OP2, goal reviews) with clear narratives that tie engineering work to business outcomes.
Shape engineering culture and standards across the wider AAE organization, beyond your own team

A day in the life
You will spend your time leading the data engineering team and shaping the data and analytics strategy alongside the Principal Data Engineer and your product and science partners. You own the people, delivery, and investment for the org; the Principal owns the deep architecture; together you set the technical direction. You will work architecture and sequencing with the Principal, turn strategy into a delivery plan and the team to execute it, meet with business partners to bring them onto the platform, and make the investment and prioritization calls on where the foundation goes next. You will also coach your engineers and managers, because leading an organization through a change in how it works is a core part of this role.

Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment.

The benefits that generally apply to regular, full-time employees include:
- Medical, Dental, and Vision Coverage
- Maternity and Parental Leave Options
- Paid Time Off (PTO)
- 401(k) Plan

If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you!

At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

BASIC QUALIFICATIONS

- Bachelor's degree
- Experience in stakeholder management, dealing with multiple stakeholders at varied levels of the organization
- Experience in written and verbal communication with the ability to present complex technical information in a clear and concise manner to executives and non-technical leaders
- 10+ years of data engineering experience with 5+ years in engineering management roles leading data engineering teams
- Experience managing teams of 15–25+ engineers across multiple levels (L4–L6), including hiring, developing, and promoting senior engineers
- Deep hands-on data engineering background — data modeling, ETL/ELT pipelines, data architecture, and warehouse operations at scale
- Experience owning both platform infrastructure and data content — accountability for dataset accuracy, data model quality, schema governance, and downstream consumer trust
- Experience owning delivery roadmaps and translating architectural strategy into executable team plans with measurable outcomes
- Experience with AWS data services (Redshift, S3, Glue, EMR, Kinesis, Lambda) or equivalent cloud data platforms
- Track record driving operational excellence: on-call management, SLA tracking, incident response, and reliability improvement

PREFERRED QUALIFICATIONS

- Experience in Redshift, or experience in managing and troublshooting network and experience in any Bigdata architecture
- Experience coordinating between technical teams, peers and business stakeholders
- Experience building and driving talent sourcing initiatives and pipelines
- Experience working in large teams or at a national/multinational organization, or experience in building financial and operational reports/data sets that inform business decision-making
- Experience leading platform modernization programs — migrating legacy data warehouses to modern architectures (serverless, lake-house, Iceberg) while maintaining business continuity
- Experience building and enforcing org-wide data content standards — data modeling conventions, dataset certification programs, data catalog management, and self-service data discovery
- Experience driving AI/ML adoption within data engineering organizations — introducing agentic frameworks, LLM-assisted development, and automation to accelerate team velocity
- Experience with cost optimization at organizational level — managing $M+ infrastructure budgets and driving efficiency improvements
- Experience operating in multi-account AWS environments with complex security, compliance, and governance requirements
- Demonstrated ability to raise the bar — have promoted engineers, built interview loops, and materially improved team capabilities and engineering culture

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.