Senior Manager, Data Engineering, AWS Analytics Engineering

Amazon; Seattle (WA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Medical, Dental & Vision coverage

Maternity & Parental leave

Paid Time Off

401(k) plan

Job summary

Amazon Development Center U.S., Inc. seeks a Senior Manager to lead a high-performing data engineering team. You will own end-to-end data pipelines, models, and curated datasets, ensuring quality, lineage, and compliance for trusted data used by AWS product leadership.

You will set the technical vision, drive on-call excellence, and grow the team as demand increases, partnering with BI, Applied Science and SDE teams and stakeholders across AWS.

Qualifications

• Bachelor's degree and 10+ years in data engineering
• Experience managing large teams (15–25+ engineers)
• Strong communication with executives and non-technical leaders
• Hands-on data engineering: modeling, ETL/ELT, warehouse operations
• Experience with AWS data services and data governance

Responsibilities

• Build and lead a high-performing data engineering team across multiple data domains
• Set technical vision and delivery roadmap with senior engineers
• Own end-to-end data solutions including pipelines and datasets
• Ensure data quality, governance, lineage, and compliance
• Manage on-call, SLAs, incident response and reliability metrics
• Collaborate with BI, Applied Science and SDE teams and business stakeholders
• Represent the team in executive reviews and cross-team initiatives
• Shape engineering culture and standards across the organization

Skills

Data engineering leadership

Stakeholder management

Executive communication

AWS data services

Education

Bachelor's degree

Tools

Redshift

S3

Glue

EMR

Kinesis

Lambda

Job description

Job ID: 10574033 | Amazon Development Center U.S., Inc.

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.

• - 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.

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