Staff Data Engineer - Data Engineering

CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com . What You'll Do: The Data Engineering Team builds and operates the foundational data infrastructure powering analytics, AI, and operational decision-making across CoreWeave. We design resilient data pipelines, scalable lakehouse systems, and high-quality datasets that enable teams across Finance, HR, Operations, and Engineering to move faster and make smarter decisions. Our mission is to make CoreWeave's data reliable, accessible, and actionable at scale. About the role: We're seeking a Staff Data Engineer to define and drive the architecture of CoreWeave's enterprise data ecosystem. You will establish the modeling, semantic, governance, and platform standards that enable teams to build trusted and reusable data products at scale. Day-to-day, you'll lead cross-domain architecture, solve the organization's most complex data design challenges, and partner with senior engineers and stakeholders to translate business requirements into durable technical systems. This role combines deep hands-on engineering expertise with company-wide technical leadership across our modern lakehouse platform. In this role, you will: Define the company-wide strategy for data modeling, semantic layers, enterprise metrics, and reusable data products. Set architectural standards for CoreWeave's lakehouse, including data organization, schemas, metadata, governance, and serving patterns. Design scalable frameworks for data quality, lineage, observability, access control, and policy enforcement. Lead the architecture of complex, cross-domain data systems spanning operational, financial, product, people, and infrastructure data. Establish durable data contracts, system boundaries, and reusable engineering patterns across teams. Identify and resolve systemic performance, reliability, and scalability constraints across the data ecosystem. Lead architecture reviews for high-impact initiatives and ensure alignment with the broader platform strategy. Evaluate and standardize core data technologies across processing, orchestration, cataloging, modeling, and metadata management. Mentor senior engineers and raise the quality of system design and technical decision-making across the organization. Who You Are: 10+ years of experience in data engineering, software engineering, distributed systems, or data architecture roles. Demonstrated experience setting technical direction for data systems spanning multiple teams, business domains, or platforms. Deep expertise in enterprise analytical modeling, including dimensional, Data Vault, semantic modeling, and governed metrics. Experience designing and operating large-scale lakehouse or streamhouse architectures using technologies such as Apache Iceberg, Delta Lake, Apache Hudi, Apache Paimon, or Apache Fluss. Advanced knowledge of distributed OLAP, query, processing, and ingestion systems such as StarRocks, ClickHouse, Trino, Spark, Flink, or Kafka. Expert-level SQL and strong programming expertise in Python, Scala, Java, or Rust, with experience building production-grade data systems. Demonstrated experience implementing governance capabilities such as lineage, profiling, metadata management, data quality controls, access policies, or auditability. Demonstrated ability to independently turn ambiguous problem statements into clear technical direction and execution plans. Track record of leading cross-cutting architecture and establishing standards adopted across multiple engineering teams. Preferred:…