What you will do
Architect & Scale: Design and maintain high-throughput detection systems and backend services, ensuring low latency and high reliability.
Data Orchestration: Lead the development of robust data pipelines using Spark and Airflow to supply detection teams with high-fidelity data for ML model training and evaluation.
Technical Leadership: Partner with Engineering Managers to define technical roadmaps, mentor junior/mid-level engineers, and drive best practices in code quality and system design.
AI-Driven Innovation: Lead the team in adopting AI productivity initiatives, leveraging tools like Claude, OpenAI, and GitHub Copilot to accelerate the development lifecycle and automate internal workflows.
Cross-functional Collaboration: Work closely with TPMs, Product Managers, Data Scientists, and Security Researchers to translate complex business needs into scalable technical solutions.
Operational Excellence: Manage and optimize cloud-native infrastructure on AWS/EKS, ensuring systems are cost-effective, secure, and performant.
Must Haves
8+ years of professional experience: Proven track record in production-level backend development with Python or Golang.
Data Engineering Expertise: Expert-level proficiency in building and optimizing distributed data pipelines using Spark and Airflow (or equivalent orchestration tools).
Cloud & Infrastructure: Extensive experience managing and scaling cloud-native applications on AWS (highly preferred), GCP, or Azure, with a proven track record of hands-on container orchestration using EKS.
Leadership & Mentorship: Experience leading technical projects, mentoring engineers, and contributing to the long-term technical strategy of a team.
System Design: Strong ability to design complex integrations and handle significant throughput/latency challenges.
Problem Solving: A methodical approach to performance debugging, benchmarking, and resolving bottlenecks in large-scale systems.
Database Proficiency: Strong experience with Postgres or similar relational databases at scale.
Education: BS in Computer Science, Applied Sciences, or a related engineering field.
Nice to Haves
Frontend Literacy: Familiarity with React and TypeScript to assist with internal tool visualizations.
Data Ecosystems: Experience with Databricks, Snowflake, or similar data lakehouse architectures.
Cybersecurity Domain: Background in threat detection, network security, or fraud prevention.
Advanced Degrees: MS in Computer Science or Electrical Engineering.