Staff Software Engineer, Data Platform
Role Overview
The Knowledge Platform team builds foundational data and AI infrastructure, covering data infrastructure, data-serving technology, and AI/ML model hosting and governance.
The staff engineer oversees strategy, architecture, development, and operation of the company’s data and AI platforms.
The team aims to make its data ecosystem AI-agent-ready and enable analytics, natural-language querying, and real-time decision-making.
The role is based in Seattle.
Key Skills for This Role
Full Job Posting
Team and role
The Knowledge Platform team builds foundational data and AI infrastructure, covering data infrastructure, data-serving technology, and AI/ML model hosting and governance.
The staff engineer oversees strategy, architecture, development, and operation of the company’s data and AI platforms.
The team aims to make its data ecosystem AI-agent-ready and enable analytics, natural-language querying, and real-time decision-making.
The role is based in Seattle.
Responsibilities
- Identify and resolve company-wide challenges using data platform solutions.
- Provide technical direction and lead hands-on solutions to complex problems.
- Advocate best practices, craftsmanship, and innovation across the data platform.
- Mentor team members and support their technical and professional development.
- Help build a high-performing team and influence data-driven and AI-powered decisions.
Minimum requirements
- At least 8 years in data platforms or equivalent work and academic exposure in a quantitative field.
- Led company-wide initiatives across multiple teams or influenced technology roadmaps.
- Collaborates across teams and stakeholders to deliver business outcomes.
- Balances execution and velocity with research, statistical understanding, and scalable design.
- Track record of mentoring scientists, engineers, and peers.
- Technical leadership involving ETL frameworks, metrics stores, infrastructure management, and data security.
- Builds, deploys, and maintains reliable multi-geographical data pipelines at scale.
- Familiarity with workflow or orchestration frameworks such as Airflow or dbt.
Preferred qualifications
- Hands-on design of data-processing patterns for a modern Lakehouse architecture.
- Experience with tools such as GCP, Databricks, BigQuery, DataProc, Kafka, Kubernetes, Spark, Dataflow, Google Cloud Storage, or Airflow.
- Strong written and verbal communication for leadership, users, and company-wide audiences.
- Ability to evaluate technologies and conduct proofs of concept; experience working in complex environments.
Compensation
- Compensation range is $200,000–$260,000 per year.
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