Engineering Manager

Censys

19d•$151,000 - $218,000•Remote

About The Position

We’re looking for an Engineering Manager to lead the Data Collection team, which builds and operates the systems that discover, scan, and collect the Internet data powering the Censys Internet Map and our customer-facing products. The team includes various engineers working across high-performance network scanning, protocol implementation, scan orchestration, real-time data pipelines and platforms, observability, and production operations. Its work directly affects the coverage, freshness, accuracy, reliability, and cost efficiency of the data our customers rely on. This independent manager will own the team’s people leadership, technical roadmap, delivery, and operational health. You will partner with Product, Research, Security, Infrastructure, and other engineering teams to identify high-value investments and deliver them effectively. You should be technically credible and able to engage in architecture, design reviews, incidents, and engineering tradeoffs while enabling engineers.

Requirements

Engineering management or team leadership experience (three years)
A track record of leading a substantial technical initiative from planning through production delivery.
Prior experience as a software or systems engineer building and operating production systems.
Strong knowledge of distributed systems and networking fundamentals, including TCP/IP, DNS, HTTP, and TLS.
Experience with data-intensive systems such as streaming services, ingestion pipelines, message queues, databases, schedulers, or high-throughput APIs.
Experience operating critical systems in production, including monitoring, incident response, capacity planning, and reliability improvement.
The technical fluency to contribute to architecture discussions, assess implementation risks, and support complex incident analysis.
Demonstrated success developing engineers through coaching, feedback, and delegation, including engineers with different specialties.
Strong project-management and communication skills, with the ability to turn broad objectives into clear priorities, milestones, and outcomes.
A bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Nice To Haves

Experience with Internet-scale scanning, network measurement, telemetry, security data, or distributed collection systems.
Experience with Cloud stacks, like GCP or similar.
Experience with streaming platforms or large-scale data stores such as Pub/Sub, Kafka, Bigtable, Spanner, or comparable technologies.
A background in cybersecurity, Internet measurement, threat intelligence, security research, or productionizing research systems.

Responsibilities

Lead, coach, and develop Scanner Engineers and Distributed Systems Engineers.
Set clear expectations for ownership, quality, collaboration, delivery, and operational responsibility.
Provide timely feedback, create meaningful growth plans, and delegate ownership of systems, projects, and decisions.
Build a healthy, accountable team; identify staffing needs; and maintain a high bar for hiring, onboarding, and performance management.
Define the Data Collection roadmap with Product, Research, Infrastructure, and engineering stakeholders.
Connect priorities to measurable improvements in data coverage, freshness, fidelity, reliability, scalability, and cost.
Keep the team focused on the highest-value work while managing scope, dependencies, staffing, and risk.
Lead major initiatives from discovery through production delivery, clearly communicating timelines, tradeoffs, and risks.
Translate research and exploratory work into maintainable production capabilities when appropriate.
Guide architecture across Internet scanning, orchestration, protocol implementations, distributed services, ingestion pipelines, APIs, and operational tooling.
Establish standards for design, testing, rollout safety, observability, service ownership, and operational readiness.
Be accountable for the quality, availability, maintainability, and cost efficiency of the team’s systems.
Define and use metrics for scan execution, pipeline health, data freshness, protocol coverage, failure rates, and resource consumption.
Support sustainable on-call and incident-management practices, ensuring follow-up addresses systemic causes.
Identify architectural bottlenecks and strategic technical debt, evaluate the cost and benefit of addressing them, and recommend priorities.