Senior Data Engineer (Data Platform)

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.

This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

Cerebras is looking for a Senior / Staff Data Engineer to own and evolve the data platform serving CBDA. The level will be calibrated to the candidate’s experience, scope, and demonstrated impact. You will design and operate the warehouse, ingestion, and transformation systems that turn operational and product data into reliable datasets for analytics, reporting, engineering decisions, and future data products.

This is a hands-on technical-leadership role. You will build production pipelines, establish durable data models and contracts, improve reliability and observability, guide technical decisions, and partner with engineering and analytics stakeholders to translate ambiguous requirements into maintainable systems.

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  Design, build, and operate scalable batch and appropriate streaming data pipelines.

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  Own ETL/ELT architecture, orchestration, warehouse models, and reusable data-engineering frameworks.

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  Lead data migrations, schema evolution, backfills, retention, archival, and recovery.

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  Handle late, duplicate, missing, and changing data through contracts, idempotency, validation, and replay.

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  Define data-quality checks, lineage, monitoring, alerting, SLAs/SLOs, and incident-response practices.

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  Improve platform performance, reliability, scalability, and cost efficiency.

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  Partner with analytics, software, infrastructure, and business stakeholders to translate requirements into durable data products.

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  Review designs and code, mentor engineers, and raise standards for testing, documentation, deployment, and operations.

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  Make and communicate long-term architecture tradeoffs across teams.

• Bachelor’s degree in computer science, engineering, or a related field, or equivalent practical experience.

• 5+ years of relevant experience. Senior candidates should demonstrate end-to-end ownership of production data systems; Staff candidates should also show cross-team architectural leadership and force-multiplier impact.

• Experience building and operating production data pipelines and warehouse systems at scale.

• Strong Python and SQL skills, including application code, transformations, query tuning, and data modeling.

• Deep knowledge of ETL/ELT, orchestration, idempotency, backfills, schema evolution, partitioning, and recovery.

• Experience creating reliable data contracts for downstream consumers, with attention to correctness, freshness, and performance.

• Experience with a modern cloud warehouse or lakehouse and AWS, GCP, or Azure.

• Strong practices in testing, data quality, observability, lineage, and production support.

• Proven system-design ability and experience leading ambiguous projects end to end.

• Strong communication and cross-functional collaboration skills.

• Experience with Databricks, Snowflake, BigQuery, or Redshift.

• Experience with Airflow, Dagster, Spark, Flink, Beam, or Kafka.

• Experience supporting both batch and near-real-time use cases.

• Familiarity with governance, cataloging, access controls, lineage, retention, and data lifecycle management.

• Experience delivering curated or semantic data layers for analytics and BI.

• Familiarity with infrastructure as code, CI/CD, containers, and reliability engineering.

• Track record of designing a platform from first principles or materially scaling an existing one.

People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

1. Build a breakthrough AI platform beyond the constraints of the GPU.

2. Publish and open source their cutting-edge AI research.

3. Work on one of the fastest AI supercomputers in the world.

4. Enjoy job stability with startup vitality.

5. Our simple, non-corporate work culture that respects individual beliefs.

Find out more about what it's like to work at Cerebras here!

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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