S

Starbucks

Runs coffeehouses and licenses its coffee brand

Director of Engineering - Data Governance

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeSeattle, WA, USANo salary listedIn-person

About the Job

Requirements

  • At least 12 years of experience in data, platform or software engineering.
  • At least 5 years of experience leading engineering managers and large distributed teams.
  • Demonstrated successful leadership of enterprise data governance transformation, including establishing a maturity framework, completing baseline assessments, developing domain roadmaps, and delivering measurable enterprise maturity improvement with several cross-functional federated teams.
  • Proven experience building enterprise-scale data platforms and governance capabilities, including cataloging, metadata, lineage, stewardship, context layers and semantic modeling.
  • Hands-on expertise with modern cloud data stacks, batch and streaming systems, and metadata platforms; experience leading insights and business intelligence teams to deliver consistent metrics, governed semantic models and trusted self-service.
  • Strong executive communication and cross-functional leadership, including influencing without direct authority, establishing shared accountability with domain owners and stewards, and driving adoption and change management in partnership with business, engineering, security and privacy.

Responsibilities

  • Establish an enterprise data governance maturity framework, baseline assessments and prioritized domain roadmaps; lead execution across several cross-functional federated teams and demonstrate measurable improvement in enterprise maturity over time.
  • Define outcomes and track progress in ownership and stewardship coverage, data quality, metadata and lineage completeness, control effectiveness and adoption; use evidence to prioritize remediation and connect governance investment to business value.
  • Develop focused data health cards describing ownership and stewardship; quality, completeness, accuracy and freshness; lineage; metadata and business context; access and privacy constraints; and readiness gaps with accountable remediation plans.
  • Use data health cards and governance controls to establish AI-ready data as a downstream outcome of trusted data foundations; partner with consuming teams to assess fitness for intended use and address readiness gaps before data supports AI-enabled experiences.
  • Partner with business leaders, engineering, analytics, security and privacy to establish shared accountability with data domain owners and stewards; bring partners together through governance councils to agree on priorities, decision rights, standards and issue resolution.
  • Influence without direct authority across federated teams, resolve competing priorities and secure commitment to domain roadmaps; lead change management, communication and enablement so governance becomes part of how partners build, manage and use data.
  • Drive development of context layers, semantic layers, enterprise data cataloging, metadata management and end-to-end lineage; make business definitions, data relationships, ownership and permitted use discoverable and consistent across domains.
  • Operationalize governance policies and controls across the data lifecycle, including classification, access, privacy, retention and quality; partner with security and privacy teams to balance trusted self-service with enterprise protection and compliance requirements.
  • Lead an insights and business intelligence team that translates business priorities into trusted reporting and actionable insights; partner with domain owners to establish consistent metrics and governed semantic models, reducing conflicting definitions and fragmented reporting.
  • Expand trusted business intelligence and self-service through discoverable, documented and governed data products; measure success through adoption, confidence in metrics and business outcomes enabled by better decisions.
  • Manage and scale a high-performing organization of engineering managers and senior individual contributors.
  • Lead globally distributed teams, including onshore/offshore engineering and vendors.
  • Foster a culture of ownership, innovation and continuous improvement.

Desired Qualifications

  • Experience with governance, cataloging and governed data discovery tools such as Collibra, Atlan, Databricks Unity Catalog and Databricks Genie; experience with every listed tool is not expected.
  • Experience working in a multi-cloud environment.
  • Experience in retail, supply chain or other high-scale operational environments.
  • Experience scaling governance councils and stewardship communities, with sustained adoption of governance practices and trusted analytics across business domains.

About the Company

Starbucks

Runs coffeehouses and licenses its coffee brand

Starbucks runs coffeehouses around the world that serve espresso drinks, brewed coffee, tea, and food, and it roasts the arabica coffee its stores sell. It grew from a single Seattle shop selling beans into the world's largest coffeehouse chain, and it helped make Italian-style espresso drinks such as lattes an everyday habit in the United States. Customers order at the counter, at drive-throughs, or ahead through the Starbucks app, whose rewards program trades points for free drinks and food. The company earns money from cafés it operates itself and from fees paid by licensed stores in places such as airports and grocery stores. Starbucks was founded in 1971 and is based in Seattle.

Company size
10,001+
Company stage
IPO
Headquarters
Seattle, WA
Founded
1971

Recently Posted Jobs

Explore more opportunities at Starbucks

See more jobs