Engineering Manager, Data Streaming

Horizon3.ai is hiring an Engineering Manager, Data Streaming to lead the team responsible for collecting, normalizing, and routing data that powers its NodeZero autonomous pentests. The role oversees engineering execution, pipeline reliability and scalability, data contracts, cross-functional coordination, and team growth.

Responsibilities:

• Lead Data Streaming: Own the engineering execution and team health for the Data Streaming sub-team. Drive sprint cadence, technical design reviews, and engineering quality for a team that processes pentest telemetry at scale
• Pipeline Reliability: Own SLOs for ingestion throughput, latency, and error rates. Build the on-call processes and runbooks that keep pipelines healthy in production
• Schema & Contract Ownership: Maintain stable ingestion schemas and data contracts between the NodeZero runner, Security Research, and the Database Platform. Evolve these contracts carefully as new pentest capabilities ship
• Scalability: Scale ingestion pipelines to handle NodeZero's growth — more customers, larger environments, new data types — without requiring proportional engineering effort to operate
• Partner cross-functionally: Work closely with Security Research (the source of new pentest data), Database Platform (the downstream consumer), and Asset Inventory (which depends on clean, normalized data) to align on data quality and pipeline design
• Team Building: Grow the sub-team to full capacity and build a culture of operational ownership: engineers who are proud of pipeline reliability, not just feature velocity

Requirements:

• Experience leading teams of 5-8 engineers building and operating data ingestion or ETL/ELT pipelines in production
• Deep understanding of streaming or event-driven data architectures — Kafka, Kinesis, Pub/Sub, or equivalent
• Experience designing data normalization and enrichment pipelines at scale, with strong attention to data quality, deduplication, and schema evolution
• Proven ability to define and own SLOs for pipeline systems: latency, throughput, error rate, and data completeness
• Experience operating data pipelines in cloud-native environments; understands the failure modes and operational patterns at scale
• Comfortable operating with autonomy — can translate ambiguous product requirements into well-scoped engineering projects
• Proficient in Go or Python; comfortable reading and writing pipeline code, not just managing those who do
• Experience with message queues and streaming systems: Kafka, Kinesis, RabbitMQ, or equivalent
• Experience with PostgreSQL or other relational databases as a pipeline destination
• Kubernetes and containerized service operations in AWS or equivalent cloud
• Familiarity with data observability tooling: pipeline monitoring, alerting, and schema drift detection
• Exposure to cybersecurity data — network scan data, vulnerability feeds, CVE/NVD schemas, or asset inventories
• Experience with graph data pipelines or knowledge graph construction
• Familiarity with CDC (Change Data Capture) patterns for downstream data consumers
• Background in data quality frameworks or data contract tooling