Principal Software Engineer, Recorded Future

• At least 5 years of experience in end to end software engineering, with additional experience in data engineering or platform engineering
• Hands-on experience with message queues and pub/sub systems such as Kafka or RabbitMQ
• Strong proficiency in Python, with experience in Go or Rust considered a plus
• Experience working with cloud infrastructure, including compute, storage, networking, and IAM fundamentals
• Experience developing REST APIs
• Proven experience architecting and delivering production-grade applications and ETL/ELT pipelines
• Strong understanding of building reliable data pipelines, including observability, error handling, and failure recovery
• Comfort working with high-volume, complex, and unstructured data
• Strong problem-solving skills, including the ability to debug both software and data issues
• Excellent attention to detail and ability to work independently while delivering high-quality results
• Interest in or familiarity with security data domains such as threat infrastructure, malware, or network telemetry
• Bachelor’s and/or Master’s degree in Computer Science, Mathematics, Statistics, Engineering, or equivalent experience

Desirable

• Recorded Future is seeking a Principal Software Engineer to join our Engineering team, responsible for building and scaling the pipelines that power the Security Intelligence Graph
• As a Principal Software Engineer, you will work across application code and infrastructure to collect, transform, and deliver structured threat data: tracking malware infrastructure, phishing campaigns, spam networks, command-and-control systems, and more
• You will play a key role in driving architectural improvements and ensuring the platform scales effectively as it continues to grow
• Data Pipeline Ownership: Design, build, and maintain scalable data pipelines that collect, transform, and deliver structured threat intelligence data powering the Security Intelligence Graph
• Architecture & Scalability: Drive meaningful architectural improvements across data and platform systems to ensure long-term scalability, reliability, and performance
• Full-Stack Engineering: Work across both application code and infrastructure, contributing to backend services, data systems, and platform capabilities
• Data Processing & Analysis: Apply statistical techniques to large-scale datasets to generate accurate and impactful insights
• Reliability & Observability: Ensure production-grade pipeline reliability through strong observability, error handling, and failure recovery practices
• Infrastructure & Deployment: Build and maintain CI/CD pipelines and contribute to infrastructure-as-code practices to support efficient and repeatable deployments
• Cross-Team Collaboration: Communicate technical concepts clearly and collaborate effectively across teams, geographies, and varying levels of technical expertise
• Technical Leadership: Engage with technical leaders across teams and contribute to raising the engineering bar through knowledge sharing and best practices