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Cylake

Data Pipeline Engineer

Sunnyvale$150k–$250kfulltimemidAdded 1 month ago

About this role

Design and build scalable data lakehouse architectures supporting petabyte-scale analytics for a early-stage cybersecurity company. You'll own end-to-end data pipelines from ingestion through transformation, working with industry veterans to deliver high-impact products.

What you'll do

  • Architect and maintain scalable, open-source data lakehouse infrastructure handling petabyte-scale workloads
  • Design end-to-end data pipelines covering ingestion, transformation, and consumption phases
  • Support both batch and real-time processing architectures
  • Ensure high performance, reliability, and data quality across systems
  • Build and run large-scale distributed data systems
  • Document solutions and communicate technical decisions effectively

What they're looking for

  • Large-scale data system architecture (petabyte scale)
  • Apache Iceberg, Parquet, Kafka, Spark, and Flink
  • Python programming
  • Data transformation and pipeline design
  • Batch and real-time processing
  • PostgreSQL and Neo4j
  • Data governance and lineage tools (preferred)
  • Cloud platform data services (preferred)

Benefits

  • Competitive compensation ($150,000–$250,000 annually)
  • Comprehensive health and well-being benefits package
  • Opportunity to grow with early-stage company
  • Work with industry veterans and experienced team
  • Equal opportunity employer with diversity commitment
  • Reasonable accommodations supported throughout hiring process
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Cylake

Cylake builds an AI-native cybersecurity platform that combines endpoint security agents, observability infrastructure, and petabyte-scale data lakehouse capabilities to detect and prevent threats across systems. The company is hiring for platform engineers, data architects, frontend engineers, and systems engineers to build core infrastructure, analytics pipelines, security agent technology, and user experiences for threat investigation.

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Likely interview questions

  • Describe a petabyte-scale data system you architected end-to-end. What were the key design decisions for ingestion, transformation, and consumption?
  • How have you handled data quality and reliability in large-scale pipelines? What tools or frameworks did you use?
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