Lead Engineer, Integrated Planning System (Seattle, WA - Hybrid)

From the beginning, Starbucks set out to be a different kind of company. One that not only celebrated coffee and 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 Lead Engineer on the Integrated Planning System team, you will help build and evolve Starbucks' planning platform that connects demand signals to supply decisions across the enterprise. You will provide technical leadership for highly scalable, distributed systems, drive architecture decisions, and partner closely with product, program, and data teams to deliver reliable, production-ready solutions that support Starbucks' supply chain and planning operations.

As a Lead Engineer, you will… • Own end-to-end delivery of product capabilities (data → services → UI), from concept to production rollout. • Lead architecture and technical decisions across streaming/ big data processing, APIs, data modeling, and user-facing experiences. • Establish an AI-enabled development workflow across requirements, design, implementation, code review, documentation, and incident/RCA support; create reusable prompts/templates and guardrails for safe, repeatable use. • Design and ship AI-assisted decision support (real-time inference, explainability, summarization, alerting) with measurable outcomes and clear human-in-the-loop controls. • Own and evolve API contracts: consistent semantics, validation at boundaries, predictable naming, additive/backward-compatible changes, and a clear deprecation strategy. • Drive data quality and identity governance: establish reliable master data practices so outputs remain consistent over time. • Own performance and reliability: define latency/throughput targets, design for backpressure/failure handling, and maintain SLOs for critical user journeys. • Mentor and level up the team through code reviews, pairing, design reviews, and coaching on system design and AI-assisted engineering best practices. • Execute cross-functionally: translate business needs into technical plans, align with partner teams , and communicate progress and tradeoffs clearly. • Deliver measurable outcomes: improve service levels, reduce operational cost, and accelerate decision cycles through better insights and automation.

Required experience • Enterprise distributed systems: 7+ years building and operating large-scale, multi-service platforms (high throughput, low latency, fault tolerant) in a complex enterprise environment. • Streaming + event-driven architecture: strong experience with Kafka (or equivalent) and stream processing (Flink / Spark Structured Streaming / Kafka Streams). • Data engineering fundamentals: building reliable pipelines, stateful processing, schema evolution, backfills, and idempotent processing patterns. (Apache Spark, Databricks, • Backend services: building production APIs and services (TypeScript/Node, Java/Kotlin, Go, or similar), caching, and performance tuning. • Datastores: strong experience with NoSQL and SQL (Postgres, Cassandra or equivalent) plus one or more of distributed caching, document stores, and/or analytics stores; understands indexing, partitioning, and query optimization. • Cloud: hands-on in cloud Azure/AWS/GCP (networking basics, IAM, managed services tradeoffs, cost/perf). • AI integration: experience integrating ML/LLM capabilities into products (model serving, prompt patterns, evaluation, safety/guardrails, monitoring, and feedback loops). • Observability + reliability: production monitoring, tracing, alerting, incident response, and defining/meeting SLOs. • Security: secure-by-default development, handling sensitive data, least-privilege access, and auditability. • Leadership: technical leadership across teams—driving design reviews, mentoring, and delivering outcomes across ambiguous problem spaces.