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

Nordstrom•Seattle, WA

7d•$166,000 - $297,000•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, access management, and cost optimization. This is a player-coach role responsible for delivery, technical standards, operational health, and engineer development while remaining hands-on with coding and automation. The Engineering Manager partners with a Product Manager who owns vision and stakeholders; the manager owns technical delivery, architecture, and team health. The team uses AI daily to write code, automate operations, and investigate incidents. It works through Kanban with continuous flow and weekly demos, without sprints or story points. Engineers are generalists who work across the portfolio, pair, cross-train, and prioritize direct contribution. The role leads engineering delivery across BigQuery/GCP, GCP Looker, Metabase, and AWS workloads; owns production reliability, incident response, on-call, capacity planning, enterprise access provisioning and governance, and cloud and tooling cost optimization. It establishes LookML governance and development practices, supports analytics teams with modeling and dashboard best practices, and leads Conversational Analytics enablement, including Golden Query standards, agent development, adoption, token utilization, and capacity planning. The manager sets technical direction, develops engineers and their AI fluency, contributes code and tooling, reviews pull requests, and owns hiring, capacity planning, and team health.

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 ongoing 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 considered perspective on how they change team workflows.

Nice To Haves

Data platform or business intelligence experience with BigQuery, Looker, Metabase, Tableau, or MicroStrategy.
Terraform and Kubernetes experience.
Cloud cost optimization at scale.
Analytics access provisioning in SailPoint IdentityNow via SCIM group synchronization.
Hands-on Dataplex zone governance.
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 Conversational Analytics enablement strategy, including Golden Query standards, agent development, and adoption.
Manage token utilization and capacity planning with Insights Delivery leadership.
Partner across the organization to drive Conversational Analytics maturity.
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.