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Salary: USD 229,500 - 260,000 / annual
Horizon3.ai is a fast-growing remote cybersecurity company building NodeZero, an autonomous penetration testing platform used by organizations ranging from educational institutions to Global 100 enterprises. The platform generates enormous volumes of structured data across vulnerability findings, asset graphs, scan histories, time-series telemetry, and customer configurations.
As Staff Software Engineer on the Database Platform team, you will be the technical anchor for database architecture and reliability across Horizon3. This is a hands-on staff-level role where you will write production code, produce architecture designs, drive critical performance and reliability decisions, and mentor mid-level engineers. You report to the Data Platform Engineering Manager.
Key responsibilities include:
- Database Architecture: Set technical direction for database systems including schema design philosophy, data modeling standards, indexing strategies, and query contract design across teams
- Performance Engineering: Own query performance across the platform by identifying slow queries, producing execution plan analyses, implementing fixes, and designing observability systems to catch regressions early
- Reliability & Operations: Own reliability engineering for all data stores including replication, failover, backup validation, capacity planning, and database incident response
- Migration Engineering: Design and own the migration framework for safe schema evolution in production with zero-downtime migrations, reversible changes, and migration testing standards
- Cross-team Enablement: Serve as database expert for application engineering teams, reviewing schemas, query patterns, and ORM usage to prevent production issues
- Mentorship: Mentor P4 engineers through design review, code review, and deep technical pairing
The team is building from a strong foundation, and your role is to raise the technical bar dramatically as data volumes and query complexity grow. You will work with PostgreSQL, graph databases (Neo4j), time-series stores (ClickHouse, TimescaleDB), and cloud-managed database services.
Horizon3 is a fully remote company with a culture built by former U.S. Special Operations cyber operators, startup engineers, and cybersecurity practitioners. The company requires a minimum 25Mbps consumer-grade broadband connection. Up to 5% travel may be required.
REQUIREMENTS:
- 8+ years of professional software engineering experience with deep specialization in database systems and data infrastructure
- Expert-level PostgreSQL proficiency including complex production query plan tuning, replication topology management, and debugging vacuum and locking issues
- Deep understanding of database architecture trade-offs: normalization vs. denormalization, indexing strategies (partial, composite, covering), partitioning, and connection pooling at scale
- Experience designing zero-downtime migration workflows in high-volume production environments
- Experience with graph databases (Neo4j or equivalent)
- Proficiency with time-series or columnar stores (ClickHouse, TimescaleDB, or equivalent) for high-throughput analytics workloads
- Track record running database systems in Kubernetes or cloud-managed environments (RDS, Cloud SQL, or equivalent) at production scale
- Proficiency in Go or Python for building tooling, access layers, and migration infrastructure
- Track record of setting engineering standards that other engineers adopt
- Ability to produce clear architecture documents and drive database design decisions across team boundaries
- Experience mentoring mid-level engineers through database design review and code review
NICE TO HAVES:
- Experience building multi-tenant database architectures with hard customer data isolation requirements
- Background in cybersecurity or experience storing and querying vulnerability, asset, or network topology data at scale
- Familiarity with eBPF-based database observability tools or advanced profiling approaches
- Experience with vector databases or embedding stores for ML-adjacent infrastructure
