Founding Data Engineer (Pipelines)
Greylock
Software Engineering, Data Science
San Jose, CA, USA
Early-stage cybersecurity company (valued at over $100M at Seed), founded by a successful serial entrepreneur, is hiring a Founding Data Engineer. Prior cybersecurity experience is a plus.
This is an opportunity to join a small, highly technical team building the company’s next-generation analytics platform from the ground up. Working closely with an experienced founding team, you will help architect and scale core data infrastructure that underpins the company’s products from day one.
What the Role Involves
Design, build, and operate a scalable, open-source data lakehouse architecture supporting petabyte-scale analytics workloads. The role spans the full data lifecycle, including ingestion, transformation, storage, and downstream consumption, with a strong focus on performance, reliability, and data quality at scale.
Qualifications
- Proven experience architecting, building, and operating large-scale distributed data systems at multi-petabyte scale
- Strong experience with both batch and real-time data processing architectures
- Hands-on experience with modern open-source lakehouse technologies, including Apache Iceberg, PostgreSQL, Neo4j, Apache Parquet, and related tooling
- Experience with distributed stream processing and analytics frameworks such as Kafka, Spark, and Flink
- Strong understanding of data transformation and pipeline orchestration patterns
- Excellent Python programming skills
- Strong written and verbal communication skills, including technical documentation
- Experience with cloud data platforms is a plus
- Familiarity with data lineage, governance, and data quality tooling is a plus
Please note:
There are no fees associated with any of the support we provide to our portfolio companies. Greylock Talent provides candidate referrals and introductions free of charge to all active investments as part of our platform support.
Due to the volume of applicants, we may not be able to respond individually unless there is a strong potential fit.