Were looking for people who are relentlessly curious and committed to continuous learning. AI is reshaping every function across our business, and we enable every team member, regardless of role or level, to build fluency in AI tools and concepts. Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes.
As a Senior Staff Software Engineer on the Agent Platform Detection team, you will be the technical visionary responsible for defining and evolving the architecture of our detection, triage, and response platform. You will lead the design and execution of backend systems and SaaS services that power alert propagation, alert lifecycle management, and response capabilities at extraordinary scale. Your technical leadership will bridge the gap between long-term architectural strategy and high-velocity product delivery, influencing how detection and response workflows are built and operated across our company's platform.
What Will You Do?
Primary responsibilities include:
Architectural Vision: Define and drive the long-term technical roadmap for the Agent Platform Detection domain, owning the architecture of backend systems that power alert ingestion, triage, lifecycle management, and response workflows across our company's SaaS platform.
System Design at Scale: Lead the design and implementation of highly available, cloud-native services that process billions of security events daily, ensuring alerts move reliably and efficiently from ingestion through investigation and action for the world's largest enterprises.
Technical Leadership & Influence: Act as a key stakeholder in cross-organizational architectural reviews, ensuring the Detection platform provides the extensibility, reliability, and observability that other product teams depend on. Drive alignment across engineering, product, and design on the technical direction of the team's feature area.
Full-Stack Depth: While backend-oriented, bring practical depth across the stack - owning and evolving frontend components in the company console built in React and TypeScript, and ensuring complex detection and response workflows are exposed in a clean, usable, and performant way.
Operational Excellence: Champion engineering best practices across the team, including advanced observability, alert health reporting, performance optimization, and the continuous improvement of runbooks, diagnostics, and incident response processes.
Mentorship & Growth: Elevate the engineering bar by mentoring Staff and Senior engineers, fostering a culture of technical excellence, accountability, and proactive problem-solving across the team.

We're looking for a Senior Data Engineer to help build next-generation data platform - the lakehouse foundation that will power data processing across the entire product. This is not a "write pipelines on top of someone else's platform" role, and it's not a pure infrastructure role either. It's both, deliberately.

You'll own the platform end to end: the infrastructure it runs on (Spark on Kubernetes, Apache Iceberg, AWS Glue, Airflow), the frameworks and tooling that let dozens of other engineers build on it without reinventing the wheel, and the design of the data pipelines themselves. Everything you build becomes leverage for the teams around you - your abstractions, base images, CI/CD flows, and operational patterns are what make the platform usable at scale.

You'll also own one of the hardest ongoing trade-offs in a high-scale data platform: balancing cost and performance. Compute sizing, storage layout, partitioning and compaction strategy, job scheduling - every decision has a price tag and a latency profile, and you'll be the one making those calls with data.

This role is ideal for an engineer who is equally comfortable debugging a Spark executor OOM on Kubernetes at 10am, designing a clean Python framework API at noon, and modeling the cost impact of a table layout change in the afternoon.



What You'll Do

Platform & Infrastructure

- Design, deploy, and operate our Spark-on-Kubernetes compute platform, including autoscaling, resource tuning, and multi-tenancy considerations.

- Own the lakehouse storage layer built on Apache Iceberg and AWS Glue catalog - table design, partitioning, compaction, schema evolution, and retention.

- Build and operate orchestration on Airflow: DAG standards, deployment flows, environment promotion, and reliability.

- Own production operations of the platform: monitoring, alerting, incident response, and continuous hardening.

Frameworks & Developer Enablement

- Build the code frameworks, libraries, and templates that other engineers use to write pipelines - so that spinning up a new production-grade Spark job is measured in hours, not weeks.

- Define and enforce standards for pipeline structure, testing, observability, and deployment across teams.

- Own CI/CD for data workloads: image builds, artifact promotion, and GitOps-based delivery.

- Act as a technical partner to product and research teams building on the platform - your customers are other engineers.

Data Pipelines & Architecture

- Design and build scalable batch and streaming pipelines processing complex, high-volume datasets from diverse sources.

- Lead large-scale backfills and migration initiatives, ensuring data consistency and integrity across evolving storage and compute platforms.

- Design event-driven data flows over large-scale queue systems (Kafka) for reliable, efficient data movement.

Cost & Performance

- Continuously balance cost against performance: right-size compute, tune queries and jobs, optimize storage layout and file sizes, and choose the correct engine for each workload.

- Build cost visibility and attribution into the platform so trade-offs are made with data, not guesswork.