Lead Data Architect Quality & Reliability

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.  

Cerebras' current customers include global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.

About The Role 

As our Lead Data Architect for Product Quality & Reliability, you will define and implement the data architecture that underpins Cerebras’s understanding of product health, reliability performance, and warranty economics across the entire lifecycle of our AI systems. 

Sitting within the Quality & Reliability (Q&R) organization, this role blends deep technical execution with high-ownership cross-functional leadership. You will partner closely with Data Engineering, Software, Inference, AI Infrastructure, Manufacturing, Test, Field Service, Customer Success, and Finance to architect a single source of truth for quality, reliability, and warranty data. You will shape how the company captures, structures, integrates, and interprets data that spans factory, lab, test, deployment, and field environments. 

Your mission is to design and operationalize the data models, ontologies, pipelines, governance standards, dashboards, and analytical frameworks that enable a unified, trustworthy narrative of quality and reliability across Cerebras’s products. This includes building and refining the data infrastructure behind warranty tracking and modeling in collaboration with Finance, ensuring accuracy, defensibility, and forward-looking insight. 

This role is well-suited for individuals who have built data foundations in hyper-growth, high-complexity hardware environments, and who thrive in architecting cohesive systems from diverse data sources, collaborating across teams, and strengthening an organization’s ability to reason about quality and reliability. 

Key Responsibilities 

1. Quality & Reliability Data Architecture

• Define and build the data models, schemas, ontologies, and canonical representations needed to unify quality, reliability, manufacturing, test, telemetry, service, and warranty data. 

• Develop a structured, non-overlapping, fully descriptive data ontology for field symptoms, defect categories, test results, reliability events, and component-level attributes. 

• Establish and maintain the single source of truth for Q&R data by defining standardized identifiers, metadata, lineage, and data governance rules across all relevant systems. 

• Architect scalable data storage and access patterns (warehouse/lake/lakehouse, curated marts) that support both high-frequency operational metrics and deep engineering analysis. 

 

1. Data Integration, Pipelines & Automation

• Build and maintain ETL/ELT pipelines (Python, SQL, Airflow/dbt or similar) that integrate data from factory systems, test infrastructure, cloud telemetry, RMAs, field service, supplier quality systems, and financial sources. 

• Implement robust data quality frameworks, including validation, anomaly detection, and monitoring for completeness, consistency, and freshness. 

• Work with internal data platform teams to leverage existing ingestion, orchestration, and catalog infrastructure while extending it to support Q&R needs. 

• Develop reusable Python-based data tooling and automation frameworks for analysts and engineers. 

 

1. Manufacturing, Supplier & CM Quality Data Architecture

• Architect the end-to-end data flow for manufacturing quality, including data f