Greylock Partners
Early‑stage, cybersecurity investment (valued over $100M at Seed), founded by a successful serial entrepreneur, is looking to hire a Founding Data Engineer. Bonus points for prior industry experience in cybersecurity.
Our ideal candidate will be a seasoned data engineering specialist (with 5+ years relevant industry experience) who can help this company create its next generation of analytics infrastructure. Working closely with a highly‑experienced founding team, you will play a central role in shaping data architecture from the ground up and will have significant room for professional growth as the organization scales.
What the Role Involves
The position centers on designing and operating a modern, open‑source data lakehouse environment built to handle extremely large datasets. The engineer will be responsible for constructing robust, end‑to‑end pipelines—from data intake to processing to end‑user access—while ensuring the platform is performant, dependable, and accurate at massive scale. Qualifications
Demonstrated expertise in building and managing very large data platforms (multi‑petabyte range) Background in both streaming and batch data processing Hands‑on familiarity with open‑source technologies commonly used in lakehouse setups (e.g., Iceberg, PostgreSQL, Parquet, graph databases such as Neo4j) Strong experience with streaming and analytics frameworks like Kafka, Spark, or Flink Solid understanding of data transformation practices Advanced proficiency in Python Clear communication skills and the ability to produce strong technical documentation Bonus: experience with cloud service provider data ecosystems Bonus: knowledge of tools related to governance, data quality, and lineage
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The position centers on designing and operating a modern, open‑source data lakehouse environment built to handle extremely large datasets. The engineer will be responsible for constructing robust, end‑to‑end pipelines—from data intake to processing to end‑user access—while ensuring the platform is performant, dependable, and accurate at massive scale. Qualifications
Demonstrated expertise in building and managing very large data platforms (multi‑petabyte range) Background in both streaming and batch data processing Hands‑on familiarity with open‑source technologies commonly used in lakehouse setups (e.g., Iceberg, PostgreSQL, Parquet, graph databases such as Neo4j) Strong experience with streaming and analytics frameworks like Kafka, Spark, or Flink Solid understanding of data transformation practices Advanced proficiency in Python Clear communication skills and the ability to produce strong technical documentation Bonus: experience with cloud service provider data ecosystems Bonus: knowledge of tools related to governance, data quality, and lineage
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