Senior Data Platform Engineer - Weights & Biases

What You’ll Do

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 our enterprise data platform.

• Design and implement scalable data systems and pipelines — 30%: Architect, develop, test, and maintain distributed batch and streaming pipelines that ingest, transform, validate, and deliver structured and semi-structured data across production systems.
• Develop data transformation and orchestration frameworks — 20%: Build and optimize workflows using technologies such as dbt, Dagster, Airflow, or comparable tools. Establish testing, deployment, dependency-management, data-quality, and observability standards for these workflows.
• Improve data warehouse performance and reliability — 15%: Design data models, schemas, partitioning strategies, and storage patterns for large-scale analytical workloads. Diagnose and resolve performance, scalability, availability, and cost-efficiency issues within multi-petabyte data environments.
• Build product telemetry and data instrumentation — 15%: Partner with software engineering teams to define event schemas and implement reliable logging, telemetry, and instrumentation for web applications, backend services, and distributed systems.
• Develop data catalog and semantic-layer capabilities — 10%: Design metadata, lineage, governance, and semantic-modeling systems that enable consistent business definitions and secure self-service analytics.
• Support data-powered AI applications — 5%: Establish data interfaces and platform foundations for internal AI applications, including systems that support automated data discovery, workflow assistance, customer-growth analysis, churn-risk modeling, and abuse detection.
• Operate and support the platform — 5%: Monitor production systems, troubleshoot incidents, document operational procedures, and participate in an on-call rotation after completing the required onboarding and training.

This position requires independent technical judgment to evaluate architectural alternatives, identify system constraints, and select appropriate data structures, algorithms, database designs, and distributed-processing patterns.

Minimum Qualifications

• 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 coursework in Engineering.
• 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 experience programming in Python, Go, Java, Scala, or another general-purpose programming 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.

Preferred Qualifications

• 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.

Pay

The base salary range for this role is $165,000 to $242,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

Benefits

• Medical, dental, and vision insurance - 100% paid for by CoreWeave
• Company-paid Life Insurance
• Voluntary supplemental life insurance
• Short and long-term disability insurance
• Flexible Spending Account
• Health Savings Account
• Tuition Reimbursement
• Ability to Participate in Employee Stock Purchase Program (ESPP)
• Mental Wellness Benefits through Spring Health
• Family-Forming support provided by Carrot
• Paid Parental Leave
• Flexible, full-service childcare support with Kinside
• 401(k) with a generous employer match
• Flexible PTO
• Catered lunch each day in our office and data center locations
• A casual work environment
• A work culture focused on innovative disruption