Data Platform Engineer Jobs in Seattle, WA

Now Brewing – senior engineer! #tobeapartner

From the beginning, Starbucks set out to be a different kind of company. One that not only celebrated coffee and the rich tradition, but that also brought a feeling of connection. We are known for developing extraordinary leaders who share this passion and are guided by their service to others.

As a Starbucks senior data & AI engineer, you’ll help turn trusted data and emerging AI capabilities into practical solutions for teams across Starbucks.

You’ll join the Enterprise Data & Analytics Platform team to build reliable data services and AI-enabled analytics experiences. Working primarily in Databricks platform, you’ll design, develop, test, deploy, and support pipelines and tools that power continuous data processing, exploration, preparation, real-time analytics, trusted self-service insights, and AI-assisted decision making. The role combines hands-on engineering with close collaboration and a shared commitment to security, quality, the partner experience, and Starbucks guiding principles.

What you’ll do:

Data Engineering

• Build dependable Lakehouse solutions with Databricks, Delta Lake, PySpark, and SQL, applying strong practices for orchestration, medallion architecture, data quality, schema evolution, and production support.
• Develop and support highly available batch, distributed, incremental, and real-time pipelines that power enterprise analytics, AI/BI, and AI-assisted business workflows.
• Work with upstream and downstream engineering teams to integrate source systems, standardize data contracts, improve lineage and observability, and make data products reliable and reusable for business and AI needs.

AI Engineering

• Design, build, and run Databricks Genie so partners can ask questions and receive trusted answers from governed enterprise data.
• Create and maintain Genie spaces, semantic models, curated datasets, dashboards, and supporting pipelines, with clear ownership, documentation, and release practices.
• Create practical ways to evaluate Genie, RAG, and agentic workflows using golden datasets, groundedness checks, regression testing, answer-quality scores, latency and cost tracking, and user feedback.
• Improve the quality of Genie and similar tools by tracing issues across prompts, semantic definitions, generated SQL, joins, metric logic, access rules, and source data.
• Build responsible AI and safety into every solution through access-aware grounding, careful handling of sensitive data, guardrails, auditability, human review, and proactive risk mitigation.

Engineering Excellence & Collaboration

• Use Unity Catalog to manage access controls, lineage, tagging, certified data products, semantic definitions, retention, and documentation so enterprise data stays secure, discoverable, reusable, and supportable.
• Investigate data and platform issues, tune performance, weigh trade-offs, and implement lasting solutions rather than short-term fixes.
• Create reliable delivery and support practices for Databricks notebooks, jobs, workflows, semantic artifacts, and AI/BI resources using CI/CD, testing, monitoring, observability, cost attribution, Declarative Automation Bundles, GitHub Actions, and environment-based release controls.
• Partner with engineering, analytics, data science, product, security, and business teams to turn real business questions into reusable metrics, certified tables, governed experiences, and dependable data contracts.
• Improve and document team coding standards, reusable frameworks, deployment patterns, and monitoring practices, and share Databricks, Genie, and AI engineering best practices across the organization.
• Mentor and support other engineers by sharing practical experience in agile delivery, DevOps, data engineering, and AI engineering.

We’d love to hear from people with:

• A bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, Data Science, Information Systems, or a related quantitative field.
• 4+ years of industry experience in data engineering, analytics engineering, AI engineering, or enterprise data-platform delivery.
• 4+ years of hands-on experience building and operating Databricks Lakehouse solutions in production. This includes experience with Databricks Workflows, notebooks, jobs, Delta Lake, Spark, PySpark, SQL, Python, data modeling, data quality, and production support.
• 1+ year designing, configuring, tuning, and supporting Databricks Genie or a similar natural-language analytics platform, with experience in semantic context, AI/BI experiences, and governed self-service analytics.
• 3+ years working with Unity Catalog governance, permissions, lineage, tagging, table certification, and governed access patterns.
• 4+ years working with SQL, dimensional and semantic modeling, metric definition, source-of-truth identification, join accuracy, and business-logic validation.
• 4+ years delivering ETL/ELT pipelines, medallion architecture, data-quality frameworks, orchestration, schema evolution, and ongoing data-product support.
• 3+ years using Declarative Automation Bundles and GitHub Actions to support CI/CD and environment promotion, including testing, monitoring, observability, and cost attribution.
• 4+ years partnering with data science, analytics, product, security, platform engineering, and business teams to deliver production outcomes.
• A customer-focused mindset, strong analytical problem-solving skills, and the ability to explain technical trade-offs clearly.

Preferred:

• Experience using AI-assisted engineering tools and techniques to accelerate data engineering, improve developer productivity, and strengthen the quality of production solutions.
• Familiarity with MLflow, Mosaic AI, model serving, vector search, RAG patterns, or agent-oriented AI workflows.
• Experience applying business knowledge and sound engineering judgment in cross-functional work.
• Experience working in retail or another high-volume customer environment.
• Familiarity with Design Thinking practices.
• Experience delivering production-grade data or AI platforms at scale.

The actual base pay offered to the successful candidate will be based on multiple factors, including but not limited to job-related knowledge/skills, experience, geographical location, and internal equity. At Starbucks, it is not typical for an individual to be hired at the high end of the range for their role, and compensation decisions are dependent upon the facts and circumstances of each position and candidate.

Join us and inspire with every cup. Apply today!

Starbucks Coffee Company is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, or protected veteran status, or any other characteristic protected by law.
Qualified applicants with criminal histories will be considered for employment in a manner consistent with all federal, state and local ordinances.

Starbucks Coffee Company is committed to offering reasonable accommodations to job applicants with disabilities. If you need assistance or an accommodation due to a disability, please contact us at applicantaccommodation@starbucks.com or 1(888) 611-2258.