Software Engineer - Data Platform (Staff+)
at Cogent Security
San Francisco, CA; New York, NY, remote, staff, full time, USD 1,00,000–3,00,000 / year, 5+ years
posted 14 Sept 2026
- Airflow
- Temporal
- Terraform
- Docker
- Apache Flink
- Apache Kafka
- Databricks
- ETL
- SQL
- Kubernetes
About the role
Remote: yes Posted: 2026-09-14
ABOUT COGENT
Cogent is an Applied AI Lab building the next generation of AI agents for cybersecurity. AI has fundamentally changed how attacks happen, allowing malicious actors to operate at unprecedented speed and scale. Cogent’s "AI Taskforce" assesses petabytes of enterprise data to remediate these issues before critical breaches occur.
To stay at the cutting edge, we blend frontier research with real-world execution. Alongside our core product work, Cogent Research serves as our applied AI lab, providing the research horsepower needed to make truly agentic security workflows a reality.
Since coming out of stealth, Cogent has experienced rapid growth. We partner with Fortune 500 companies to secure some of the most complex production environments in the world.
We’re backed by Greylock and we’ve built a team with the best minds in applied AI. Our team is comprised of people from:
- Top universities like Stanford, Berkeley, Penn, Duke, Carnegie Mellon, Waterloo
- Unicorn, high-growth companies like Scale AI, Databricks, Stripe, Tesla, Coinbase
- World class cybersecurity experts from Wiz, Abnormal AI, Zscaler
- Preeminent research labs like Deepmind and SAIL
We are looking for Founding and Staff-level Engineers to design and implement the foundational pillars of Cogent’s data platform and integration pipeline in order to reliably ingest and normalize enterprise data sources across many corporate and IT systems and then to enable the transformation of that data into a knowledge base that can be leveraged by other GenAI systems.
What you would do
- Build the new standard for ingesting and extracting enterprise data
- Your role will involve building data connectors, running data pipelines, and extracting insights and context from our customer’s enterprise environments.
- You should get excited about building robust and secure backend systems that handle large volumes of data, ETLs, as well as reimagining how enterprise security teams should extract, store and access data.
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