Engineering Manager: Engineering Data Platform Enablement (Hybrid, Seattle - WA)

Nordstrom•Seattle, WA

5d•Hybrid

About The Position

Data Platform Enablement keeps Nordstrom's analytics and data platforms running, evolving, and getting simpler. The team owns a broad portfolio across GCP and AWS, including BigQuery, Looker Core, Metabase, and the access management and cost optimization needed to maintain those platforms. This is a player-coach role responsible for delivery, technical standards, operational health, and engineer development, while remaining hands-on with code and automation. The role partners with a Product Manager who owns vision and stakeholders; the Engineering Manager owns technical delivery, architecture, and team health. The team uses AI daily for coding, operations automation, and incident investigation, and works in a Kanban model with continuous flow and weekly demos. The role leads platform and operations across BigQuery/GCP, GCP Looker, Metabase, and AWS workloads; owns reliability, incident response, and production on-call; plans capacity and compute costs; and supports deprecation of legacy platforms. It also owns enterprise access provisioning and governance, cost optimization, Looker governance and development practices, and Conversational Analytics enablement. The manager sets technical direction, develops engineers and their AI fluency, contributes directly as an engineer, and owns hiring, capacity planning, and team health. The position is hybrid in Seattle, Washington. The listed annual pay range is $166,000–$258,000 for Engineering Manager and $191,000–$297,000 for Sr Engineering Manager. Pay offers depend on location and job-related knowledge, skills, and experience. The position may be eligible for performance-based incentives or bonuses. Nordstrom offers medical, vision, dental, retirement, paid time away, life insurance, disability coverage, merchandise discounts, and EAP resources; eligibility requirements may apply.

Requirements

At least 2 years leading engineering teams, with direct accountability for delivery and people development.
At least 5 years building software, platforms, infrastructure, or data systems.
Hands-on Python proficiency or equivalent, with current coding and direct technical contribution expected.
Strong cloud experience across GCP and/or AWS.
Fluency in Git, CI/CD, and infrastructure as code.
Experience owning access management programs at scale.
Daily use of AI coding tools and a clear perspective on how they change the way teams work.

Nice To Haves

Data platform or business intelligence experience with BigQuery, Looker, Metabase, Tableau, or MicroStrategy.
Experience with Terraform and Kubernetes.
Experience with cloud cost optimization at scale.
Experience with analytics access provisioning in SailPoint IdentityNow via SCIM group synchronization.
Hands-on Dataplex zone governance experience.
Experience establishing an AI-first engineering practice.
Familiarity with Conversational Analytics, LLM-powered data tools, or semantic layer patterns.
Experience at a large-scale retail, e-commerce, or consumer organization.

Responsibilities

Lead engineering delivery across BigQuery/GCP, GCP Looker, Metabase, and AWS workloads.
Own platform reliability, incident response, and on-call for production systems.
Drive capacity planning by forecasting growth, managing compute costs, and scaling ahead of demand.
Support deprecation of legacy platforms, including MicroStrategy and Tableau.
Own enterprise access provisioning and governance across the data platform.
Lead cost optimization across GCP, AWS, and platform tooling in partnership with engineering and finance.
Define access policies, tiering models, and provisioning workflows with security and compliance partners.
Establish LookML governance, development standards, and deployment practices for Looker Core.
Partner with analytics teams on explore design, data modeling, and dashboard best practices.
Own platform health, migration governance, and the Looker development lifecycle.
Lead the Conversational Analytics enablement strategy, including Golden Query standards and agent development and adoption.
Manage token utilization and capacity planning with Insights Delivery leadership.
Partner with teams across the organization to drive Conversational Analytics maturity enterprise-wide.
Set technical direction for architecture, standards, monitoring, and prioritization.
Develop engineers and coach AI fluency through hands-on guidance and a high adoption bar.
Write code, build tooling, and review pull requests alongside the team.
Own hiring, capacity planning, and team health.