Staff Software Engineer - Data Engineering
Denver, Colorado, USAAll locationsDenver, Colorado, USADenver CO Hybrid
$170,000–$190,000 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
The Data team at Todyl owns the systems that turn high-volume security telemetry into something the rest of the platform and our partners can act on. We design and operate the ingestion pipelines, storage, and search and analytics layer that power detection, investigation, and the business intelligence our AI/ML and product teams build on. Our work is judged by how reliably and quickly data lands, stays correct, and stays queryable at scale, not by how many tickets we close. This is a staff-level individual contributor role and the technical anchor for the Data team. You will set the technical direction for our data ingestion and storage systems, own the most complex and ambiguous initiatives end-to-end, and operate as a multiplier who raises the capability of the whole team. You will work as a peer to Architecture and to the AI/ML and product teams on decisions that shape how Todyl handles data over the next several years, and you will be the person other engineers go to when a problem is too hard or too ambiguous for anyone else to resolve. This is a technical leadership role, not a people-management one. We do not expect deep knowledge across every item below, but familiarity with several of these will help you ramp quickly. Most importantly, we are looking for a strong technical background, the willingness to learn what you do not already know, and demonstrated experience setting technical direction and building data systems at meaningful scale.
What you'll bring
- 8+ years of end-to-end commercial software and application development
- 3+ years of commercial software development for a multi-tenant cloud platform
- Demonstrated experience owning complex, ambiguous initiatives end-to-end and setting technical direction for a team
- Go and JavaScript, and software development under Linux or Unix-based systems
- Python and SQL for data work
- Data ingestion and streaming pipelines under high volume and velocity
- Data storage and modeling: relational and columnar stores, data warehouses, and search and analytics engines (Quickwit, Elasticsearch)
- AWS and cloud-native, multi-tenant infrastructure
- Observability for data systems (Grafana, Prometheus)
- Git and modern development workflows