Senior engineer, Planning Demand; Seattle, WA)

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 Senior Engineer, you will help build and evolve Starbucks’ Integrated planning system that connects demand signals to supply decisions across the enterprise. You will design and deliver scalable, distributed solutions across data, services, and user experiences, while contributing to architecture and engineering standards that enable reliable planning and supply chain operations.

You will work closely with product, program, data, and engineering partners to translate complex business needs into production-ready capabilities. You will also help advance the team’s use of AI-assisted engineering and AI-enabled decision support, while maintaining strong standards for reliability, security, data quality, and maintainability.

• Own complex product capabilities end to end, spanning data pipelines, backend services, APIs, and user-facing experiences, from technical design through production rollout and ongoing support.
• Design scalable, distributed solutions using event-driven and streaming architectures, big-data processing, APIs, data models, and modern application patterns.
• Contribute to architecture and technical direction, evaluating design alternatives and making sound engineering trade-offs across scalability, performance, reliability, maintainability, and cost.
• Apply AI-assisted engineering practices throughout requirements analysis, design, implementation, testing, code review, documentation, and incident/RCA activities, while helping develop reusable patterns and appropriate guardrails.
• Build AI-assisted decision-support capabilities, including inference, summarization, explainability, alerting, and human-in-the-loop experiences, with measurable business and operational outcomes.
• Design and evolve APIs and service contracts with consistent semantics, boundary validation, predictive naming, backward compatibility, and appropriate versioning and deprecation practices.
• Strengthen data quality and identity consistency by implementing reliable master-data, validation, schema, and data-governance patterns across planning workflows.
• Engineer for performance and reliability, defining and meeting latency, throughput, availability, and resilience requirements; designing for back pressure, retries, idempotency, observability, and failure recovery.
• Build production-ready software with effective monitoring, tracing, alerting, automated testing, security controls, and operational readiness.
• Raise engineering quality across the team through thoughtful code reviews, design reviews, pairing, technical documentation, knowledge sharing, and mentoring.
• Partner effectively across functions, translating business and product requirements into actionable technical designs and communicating dependencies, risks, progress, and engineering trade-offs clearly.
• Drive measurable outcomes by improving service levels, reducing operational complexity and cost, accelerating decision cycles, and enabling better planning decisions through automation and actionable insights.

• Distributed systems: 7+ years of software engineering experience, including designing, building, and operating large-scale distributed or multi-service systems with demanding scalability, performance, availability, and fault-tolerance requirements.
• Streaming and event-driven architecture:
  Strong experience with Apache Kafka or equivalent event-streaming technologies, along with stream-processing technologies such as Apache Flink, Apache Spark Structured Streaming, or Kafka Streams.
• Data engineering:
  Experience building reliable data pipelines and distributed processing solutions, including stateful processing, schema evolution, backfills, data validation, and idempotent processing. Experience with technologies such as Apache Spark and Databricks is strongly preferred.
• Backend engineering:
  Strong experience developing production APIs and services using Type Script/Node.js, Java/Kotlin, Go, or comparable languages and frameworks, including caching, concurrency, resiliency, and performance optimization.
• Data stores:
  Strong experience with relational and No
  
  SQL technologies such as PostgreSQL and Cassandra, or…