senior engineer, Planning Demand (Seattle, WA)

Starbucks Coffee Company•Seattle, WA

3d•$132,100 - $220,200

About The Position

As a Senior Engineer, you will help build and evolve Starbucks’ integrated planning system, connecting demand signals to supply decisions across the enterprise. You will design and deliver scalable, distributed solutions across data, services, and user experiences, contributing to architecture and engineering standards that support reliable planning and supply chain operations. You will partner with product, program, data, and engineering teams to translate complex business needs into production-ready capabilities. The role also advances AI-assisted engineering and AI-enabled decision support while upholding standards for reliability, security, data quality, and maintainability.

Requirements

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.
Strong experience with Apache Kafka or equivalent event-streaming technologies and stream-processing technologies such as Apache Flink, Apache Spark Structured Streaming, or Kafka Streams.
Experience building reliable data pipelines and distributed processing solutions, including stateful processing, schema evolution, backfills, data validation, and idempotent processing.
Strong experience developing production APIs and services using TypeScript/Node.js, Java/Kotlin, Go, or comparable languages and frameworks, including caching, concurrency, resiliency, and performance optimization.
Strong experience with relational and NoSQL technologies such as PostgreSQL and Cassandra, or comparable platforms; understanding of indexing, partitioning, query optimization, distributed caching, document stores, and/or analytical data stores.
Hands-on experience with Azure, AWS, or GCP, including cloud-native services, IAM, networking fundamentals, resiliency patterns, and cost/performance trade-offs.
Experience integrating ML or LLM capabilities into production applications, including model integration or serving, prompt patterns, evaluation, safety and guardrails, monitoring, and feedback loops.
Experience implementing production monitoring, distributed tracing, logging, alerting, incident response, root-cause analysis, and service-level objectives (SLOs).
Strong understanding of secure-by-default software development, sensitive-data handling, least-privilege access, secrets management, and auditability.
Demonstrated ability to produce maintainable, testable production software and improve engineering practices through automated testing, CI/CD, code reviews, technical documentation, and operational readiness.
Demonstrated ability to influence technical decisions, contribute to design reviews, mentor engineers, and deliver complex solutions across ambiguous problem spaces.
Proven ability to collaborate with product, program, data, platform, and engineering teams to turn complex requirements into reliable production solutions.

Nice To Haves

Experience with Apache Spark and Databricks is strongly preferred.

Responsibilities

Own complex product capabilities end to end across 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 alternatives and balancing scalability, performance, reliability, maintainability, and cost.
Apply AI-assisted engineering throughout requirements analysis, design, implementation, testing, code review, documentation, and incident and root-cause analysis; develop reusable patterns and 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, predictable naming, backward compatibility, and appropriate versioning and deprecation practices.
Improve data quality and identity consistency through reliable master-data, validation, schema, and data-governance patterns across planning workflows.
Engineer for performance and reliability by defining and meeting latency, throughput, availability, and resilience requirements, and designing for backpressure, retries, idempotency, observability, and failure recovery.
Build production-ready software with monitoring, tracing, alerting, automated testing, security controls, and operational readiness.
Raise engineering quality through code and design reviews, pairing, technical documentation, knowledge sharing, and mentoring.
Partner across functions to translate business and product requirements into technical designs and communicate dependencies, risks, progress, and engineering trade-offs.
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.

Benefits

Medical, dental, and vision insurance
Basic and supplemental life insurance
Other voluntary insurance benefits
Short-term and long-term disability
Paid parental leave
Family expansion reimbursement
Paid vacation from date of hire
Sick time accrued at 1 hour for every 25 hours worked
Eight paid holidays and two personal days per year
401(k) retirement plan with employer match for eligible partners
Discounted company stock program (S.I.P.)
Starbucks equity program (Bean Stock)
Incentivized emergency savings and financial well-being tools
100% upfront tuition coverage for a first-time bachelor’s degree through Arizona State University’s online program via the Starbucks College Achievement Plan
Student loan management resources and access to other educational opportunities
Backup care
DACA reimbursement