Job description

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. We partner with Fortune 500 companies to secure some of the most complex production environments in the world. We're backed by Greylock & Bain Capital Ventures, 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 ABOUT THE ROLE We're hiring a Senior Storage Infrastructure Engineer on our Core Platform team to own how we store, protect, and operate data at scale. You'll build the backup, observability, and operations layer across our relational, streaming, and analytical stores - and make it agent-forward. This is a hands-on role for an engineer with deep data infrastructure experience, ideally sharpened at a data-focused startup. WHAT YOU'LL DO - Standardize backup, restore, retention, and encryption policies across Aurora / RDS, Clickhouse, and MSK - Build storage observability dashboards for capacity, performance, replication, lag, and cost - Create a Storage Operations Agent for incident triage, slow-query investigation, and remediation workflows WHAT WE'RE LOOKING FOR - Deep experience with data stores: Aurora RDS, Clickhouse, Kafka / AWS MSK, and Postgres - Experience with table formats and lakehouse patterns, including Iceberg - Strong coding in Python and Golang - Infrastructure-as-code skills with Terraform - 3-6 years at a successful tech company (Senior, Staff+) BONUS POINTS - 1-2 years at a data-focused startup (Databricks, Snowflake) - Object and analytical storage experience: S3, GCS, Google BigQuery, AWS Athena, Presto, Parquet - Vector and graph stores: VectorDB, Turbopuffer, Puppygraph - A secondary spike in Observability Infrastructure FOR CALIFORNIA BASED APPLICANTS The standard base salary range for this position is $100,000 - $300,000 annually. Compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. We are committed to building an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.

Requirements

Must-haves

This is a hands-on role for an engineer with deep data infrastructure experience, ideally sharpened at a data-focused startup.
Standardize backup, restore, retention, and encryption policies across Aurora / RDS, Clickhouse, and MSK
Build storage observability dashboards for capacity, performance, replication, lag, and cost
Create a Storage Operations Agent for incident triage, slow-query investigation, and remediation workflows
Deep experience with data stores: Aurora RDS, Clickhouse, Kafka / AWS MSK, and Postgres
Experience with table formats and lakehouse patterns, including Iceberg
Strong coding in Python and Golang
Infrastructure-as-code skills with Terraform
3-6 years at a successful tech company (Senior, Staff+)
1-2 years at a data-focused startup (Databricks, Snowflake)
We are committed to building an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.

Nice-to-haves

Object and analytical storage experience: S3, GCS, Google BigQuery, AWS Athena, Presto, Parquet
Vector and graph stores: VectorDB, Turbopuffer, Puppygraph
A secondary spike in Observability Infrastructure

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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Platform: ashby
Effort: quick