Senior Software Engineer, Server Fleet Infrastructure

CoreWeave•Sunnyvale, CA

6d•$153,000 - $204,000

About The Position

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. The Fleet Monitoring and Analysis (FMA) team builds and operates the forward-deployed monitoring and observability layer for CoreWeave’s ever-expanding global hardware fleet; continually improving node and environmental visibility to support automated provisioning and high-reliability operations. As a Senior Data Engineer you will own and evolve the data lake and analytics stack that powers observability and decision-making for CoreWeave’s global hardware fleet. You’ll maintain, monitor, and upgrade our data lake infrastructure (Apache Iceberg, Trino, Apache Airflow, Apache Spark, Apache Superset) and ETL pipelines, while delivering ad-hoc analysis and executive-ready reporting for team, director, and leadership stakeholders. You’ll also create visualizations, documentation, and integrations that make fleet monitoring data reliable, discoverable, and actionable across the organization.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field.
4 - 7 years of experience as a Data Engineer or in a similar data-focused role in a fast-paced environment.
Strong SQL skills for data manipulation, modeling, and querying large datasets.
Proficiency in at least one programming language commonly used for data engineering such as Python, Java, or Scala.
Hands-on experience with data pipeline orchestration tools (e.g., Apache Airflow) and big data technologies (e.g., Apache Spark).
Experience designing, operating, and optimizing data lake and/or data warehouse solutions, with a solid understanding of data modeling and performance tuning.
Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and related data services (e.g., object storage, managed databases, analytics services).
Familiarity with database systems (e.g., SQL and NoSQL) and data warehousing concepts, including partitioning, indexing, and schema design.
Experience building, maintaining, and monitoring ETL/ELT pipelines in production environments, including alerting and observability.
Experience creating and maintaining reporting and analytics solutions (dashboards, reports, and metrics) for technical and non-technical audiences.

Nice To Haves

Experience with modern data lakehouse technologies and table formats such as Apache Iceberg (or similar technologies like Delta Lake or Apache Hudi).
Experience with Trino or other distributed SQL query engines at scale.
Experience with Apache Superset or other BI/visualization tools for building self-service analytics.
Experience with data quality frameworks, data observability tooling, and/or metadata management.
Experience supporting executive-level reporting and KPI design in partnership with business and finance stakeholders.

Responsibilities

Design, develop, and maintain robust and scalable data pipelines to collect, process, and store data from various sources, including APIs, databases, and third-party services.
Maintain, monitor, and upgrade CoreWeave’s data lake infrastructure, including Apache Iceberg, the Trino query layer, Apache Airflow, Apache Spark, and Apache Superset.
Maintain, monitor, and upgrade ETL/ELT pipelines to ensure reliable, performant, and observable data flows across batch and (where applicable) streaming workloads.
Create and optimize data models and data products to support analytics and reporting, ensuring data accuracy, consistency, and performance.
Provide ad-hoc analysis and reporting for team, director, and executive-level stakeholders, translating business questions into data-driven insights and clear narratives.
Create visualizations and dashboards (e.g., in Apache Superset or similar tools) that surface key metrics, trends, and operational KPIs for a variety of internal audiences.
Develop and maintain documentation and runbooks for data pipelines, data lake infrastructure, data models, and usage patterns to support knowledge sharing and troubleshooting.
Implement data security and governance best practices to protect sensitive information and comply with data privacy regulations.
Collaborate with cross-functional teams to integrate data into applications and analytics platforms, helping to visualize performance metrics and identify opportunities for improvement.