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# Senior Data Engineer (Hardware Data Platform)

- **Company:** Precision Inc.
- **Location:** Sunnyvale, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, BigQuery, Continuous Integration, Data Centers, Data Infrastructure, Extract Transform Load (ETL), Information Lifecycle Management, Python (Programming Language), Operational Databases, Query Optimization, Reliability Engineering, Standard Sql, Data Streaming, Snowflake, Apache Spark, Data Layers, Information Technology, Apache Flink, Apache Kafka, Hardware Acceleration, Hardware Infrastructure, Databricks
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_c962f8b8a9dd64fe485abbbf6e6954e3

## About the Role

* 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 working with hardware companies.
 * Experience working on server hardware, AI accelerators, hardware accelerator, datacenter, AI Hardware, enterprise product, GPU, CPU, TPU, server platform etc., * 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.

## Description

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

 * Own ETL/ELT architecture, orchestration, warehouse models, and reusable data-engineering frameworks.
 * Lead data migrations, schema evolution, backfills, retention, archival, and recovery.
 * Handle late, duplicate, missing, and changing data through contracts, idempotency, validation, and replay.
 * Define data-quality checks, lineage, monitoring, alerting, SLAs/SLOs, and incident-response practices.
 * Improve platform performance, reliability, scalability, and cost efficiency.
 * Partner with analytics, software, infrastructure, and business stakeholders to translate requirements into durable data products.
 * Review designs and code, mentor engineers, and raise standards for testing, documentation, deployment, and operations.
 * Make and communicate long-term architecture tradeoffs across teams.