Senior Data Platform Engineer - Weights & Biases

CoreWeave•Sunnyvale, CA

17h•$165,000 - $242,000

About The Position

As a Senior Data Platform Engineer, you will apply advanced knowledge of software engineering, data architecture, distributed systems, and database technologies to design and operate CoreWeave’s enterprise data platform. You will make independent technical decisions about architectural alternatives, system constraints, data structures, algorithms, database designs, and distributed-processing patterns. The role focuses on building scalable batch and streaming data pipelines; developing transformation and orchestration frameworks; improving data warehouse performance and reliability across multi-petabyte environments; and partnering with software engineering teams on product telemetry and instrumentation. You will also develop data catalog, metadata, lineage, governance, and semantic-layer capabilities, establish foundations for internal AI applications, and monitor and support production systems. After onboarding and training, you will participate in an on-call rotation.

Requirements

Bachelor’s degree or foreign equivalent in Computer Science, Computer Engineering, Data Science, Information Systems, or a closely related technical or quantitative field involving substantial engineering coursework.
At least five years of progressive professional experience in software engineering, data engineering, data infrastructure, or a closely related occupation.
Professional experience designing and implementing production data pipelines and distributed data-processing systems.
Professional experience with data transformation or workflow-orchestration technologies such as dbt, Dagster, Airflow, or comparable platforms.
Professional experience designing data models, schemas, and storage strategies for large-scale analytical databases or cloud data warehouses.
Professional programming experience in Python, Go, Java, Scala, or another general-purpose language used to build production data systems.
Professional experience with SQL, database performance optimization, data-quality testing, and production monitoring.
Knowledge of software-engineering practices, including version control, automated testing, code review, continuous integration, deployment automation, and technical documentation.
Knowledge of web-application telemetry, event instrumentation, logging systems, APIs, and distributed-service architecture.
Ability to communicate technical designs, risks, and tradeoffs to engineering, data science, analytics, and business stakeholders.

Nice To Haves

Experience operating multi-petabyte data platforms or other high-volume, business-critical data systems.
Experience with cloud infrastructure and data services on AWS, Google Cloud Platform, or Microsoft Azure.
Experience with data catalogs, metadata management, lineage systems, governance frameworks, or semantic layers.
Knowledge of machine-learning workflows, model-development terminology, or the data requirements of production AI applications.
Experience supporting internal platform users, responding to technical questions, and converting user feedback into platform improvements.
Demonstrated ability to work effectively in a self-directed environment, identify opportunities for improvement, and collaborate across engineering and business teams.

Responsibilities

Architect, develop, test, and maintain distributed batch and streaming pipelines that ingest, transform, validate, and deliver structured and semi-structured data across production systems.
Build and optimize data workflows using dbt, Dagster, Airflow, or comparable tools; establish standards for testing, deployment, dependency management, data quality, and observability.
Design data models, schemas, partitioning strategies, and storage patterns for large-scale analytical workloads; resolve performance, scalability, availability, and cost-efficiency issues in multi-petabyte data environments.
Partner with software engineering teams to define event schemas and implement reliable logging, telemetry, and instrumentation for web applications, backend services, and distributed systems.
Design metadata, lineage, governance, and semantic-modeling systems that support consistent business definitions and secure self-service analytics.
Establish data interfaces and platform foundations for internal AI applications, including automated data discovery, workflow assistance, customer-growth analysis, churn-risk modeling, and abuse detection.
Monitor production systems, troubleshoot incidents, document operational procedures, and participate in an on-call rotation after completing required onboarding and training.