Data Engineer

WHY COMO COMO is building the future of cyber resilience. Our AI-native platform helps organizations understand their ability to recover from cyberattacks, identify critical gaps and dependencies, and prepare for what happens after an attack. Backed by a significant seed round led by General Catalyst, with a live product and paying customers, we're building an exceptional team to define a new category in cybersecurity. ABOUT THE ROLE We're looking for a Data Engineer to build and own COMO's data pipelines: the pipelines that turn raw cloud and Kubernetes telemetry into the dependency map at the heart of the product, the answer to "what does this system depend on, and will it come back after an attack?" This is hands-on pipeline engineering over infrastructure telemetry at real enterprise scale, where a careless query isn't slow, it's expensive, and where the data is often wrong by design. You'll design, build, and run these pipelines end to end, alongside the engineers behind our collectors and graph engine. WHAT YOU'LL DO - Design and build the data pipelines that turn flow logs, cloud configuration, and Kubernetes data into service-level dependencies with confidence scores - Own these pipelines in production: reliability, monitoring, data quality, and cost - Make attribution provable: handle recycled identities, late and out-of-order data, and the places where cloud telemetry misattributes traffic - Engineer for cost: partitioning, incremental processing, and sampling that keep a scan-heavy workload inside a budget - Work closely with the engineers behind our Kubernetes collectors and graph engine, using AI tooling throughout the development lifecycle WHAT WE'RE LOOKING FOR - 4+ years of experience in a Data Engineering role, building and running data pipelines over large-scale log, event, or telemetry data - Experience building pipelines over raw cloud or network telemetry at scale (VPC Flow Logs, CloudTrail, NetFlow, or similar) with Athena, BigQuery, Spark, ClickHouse, or equivalent - Deep SQL: window functions, temporal joins, partitioning, and scan-cost control - Cloud-native production experience (AWS or Azure) - Experience owning a data pipeline end to end: design, production, operation, and cost - Kubernetes familiarity (Pods, Services, networking) - advantage - Security, observability, or network-analytics data background - advantage OUR STACK Python, Polars, Athena / SQL over S3, Kafka, PostgreSQL, AWS and Azure APIs, Kubernetes, with an AI-native workflow throughout.