senior engineer, Enterprise Data & Analytics Platform (Seattle, WA)

Starbucks Coffee Company•Seattle, WA

17h•$132,100 - $220,200

About The Position

As a Starbucks senior data and AI engineer, you will turn trusted data and emerging AI capabilities into practical solutions for teams across Starbucks. You will join the Enterprise Data & Analytics Platform team and primarily use Databricks to build reliable data services and AI-enabled analytics experiences. The work includes designing, developing, testing, deploying, and supporting pipelines and tools for continuous data processing, exploration, preparation, real-time analytics, self-service insights, and AI-assisted decision-making. The position combines hands-on engineering with close collaboration and a focus on security, quality, partner experience, and Starbucks guiding principles. You will build Lakehouse solutions and production data pipelines; create and support Databricks Genie experiences and their semantic models, datasets, dashboards, and pipelines; and establish ways to evaluate natural-language and AI workflows. You will use Unity Catalog and responsible-AI practices to keep enterprise data secure, governed, discoverable, and reusable. You will also improve deployment, testing, monitoring, observability, and support practices, collaborate across technical and business teams, document standards and reusable patterns, and mentor other engineers.

Requirements

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

Nice To Haves

Experience using AI-assisted engineering tools and techniques to accelerate data engineering, improve developer productivity, and strengthen production quality.
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.

Responsibilities

Build dependable Databricks Lakehouse solutions using Delta Lake, PySpark, and SQL, with 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 for enterprise analytics, AI/BI, and AI-assisted business workflows.
Integrate source systems with upstream and downstream teams; standardize data contracts and improve lineage, observability, and the reliability and reusability of data products.
Design, build, and operate 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.
Evaluate Genie, RAG, and agentic workflows using methods such as golden datasets, groundedness checks, regression testing, answer-quality scores, latency and cost tracking, and user feedback.
Trace and improve issues across prompts, semantic definitions, generated SQL, joins, metric logic, access rules, and source data.
Build responsible AI and safety into solutions through access-aware grounding, sensitive-data handling, guardrails, auditability, human review, and risk mitigation.
Use Unity Catalog to manage access controls, lineage, tagging, certified data products, semantic definitions, retention, and documentation.
Investigate data and platform issues, tune performance, assess trade-offs, and implement lasting solutions.
Establish 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 develop reusable metrics, certified tables, governed experiences, and dependable data contracts from business needs.
Improve and document coding standards, reusable frameworks, deployment patterns, and monitoring practices, and share Databricks, Genie, and AI engineering best practices.
Mentor and support engineers by sharing practical experience in agile delivery, DevOps, data engineering, and AI engineering.

Benefits

Medical, dental, and vision insurance.
Basic and supplemental life insurance and other voluntary insurance benefits.
Short-term and long-term disability.
Paid parental leave and family expansion reimbursement.
Paid vacation from date of hire and sick time accrued at 1 hour for every 25 hours worked.
Eight paid holidays and two personal days per year.
Eligible partners may participate in a 401(k) retirement plan with employer match.
Discounted company stock program (S.I.P.) and 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 and DACA reimbursement.