Backend Engineer Iii- Falcon Exposure Management

CrowdStrike CrowdStrike · Enterprise · Bangalore, India

Backend Engineer III for CrowdStrike's Falcon Exposure Management team, focusing on building a next-generation Cyber Asset Attack Surface Management (CAASM) platform. The role involves architecting and developing a cloud-native integration platform that connects with numerous security vendors and cloud providers, processes high volumes of events, and models diverse assets in real-time. Key aspects include building large-scale data pipelines, developing AI-powered integration tools with agentic workflows, implementing intelligent entity resolution using ML, and designing self-learning systems. The platform aims to create a unified view of organizational risk and serve as the central nervous system for enterprise security operations.

What you'd actually do

  1. Build integration infrastructure at unprecedented scale: Architect and develop a complete integration platform to seamlessly connect with hundreds of third-party security vendors, cloud providers, and enterprise tools—creating the most comprehensive security data fabric in the industry.
  2. Engineer large-scale data pipelines: Design and implement high-performance data processing systems capable of ingesting and correlating hundreds of thousands of events per second in real-time across massive enterprise environments.
  3. Solve complex data modeling challenges: Model and normalize the incredibly diverse universe of assets, such as Cloud, at scale—from compute instances and containers to IAM policies, network rules, storage buckets, and identity permissions.
  4. Develop AI-powered integration tools: Build agentic workflows and Model Context Protocols (MCPs) to create AI-powered development tools that accelerate our ability to onboard new data sources and automate complex transformations.
  5. Design intelligent entity resolution systems: Implement AI-driven entity resolution and deduplication that intelligently identifies and merges duplicate assets across disparate data sources using advanced machine learning techniques.

Skills

Required

  • 7+ years of production-level experience building, delivering, and maintaining distributed systems at scale
  • Strong proficiency in Go, Python, or Java with demonstrated experience architecting microservices and distributed systems
  • Hands-on expertise with Kubernetes, Docker, and infrastructure-as-code tools (Terraform, CloudFormation, etc.)
  • Proven experience building high-throughput data processing systems capable of handling hundreds of thousands of events per second
  • Deep understanding of event-driven architectures, stream processing, and message queuing systems (Kafka, RabbitMQ, Kinesis, etc.)
  • Experience with complex data modeling, schema design, and data normalization at scale
  • Strong background in building integration platforms, ETL pipelines, or data ingestion frameworks
  • Familiarity with data mapping and transformation languages (CEL, JSONPath, JOLT, or similar expression languages)
  • Excellent problem-solving skills with the ability to tackle ambiguous, complex technical challenges
  • Strong communication and collaboration skills in a remote-first, globally distributed environment

Nice to have

  • Previous experience in cybersecurity, vulnerability management, asset management, or GRC domains
  • Expertise with GenAI/LLM technologies and their practical application in software development and automation
  • Experience building agentic AI systems, autonomous workflows, or working with Model Context

What the JD emphasized

  • AI-driven automation
  • agentic workflows
  • intelligent entity resolution
  • machine learning-powered data quality systems
  • large scale distributed systems
  • cloud-native platform
  • hundreds of third-party security vendors
  • hundreds of thousands of events per second
  • complex data modeling challenges
  • AI-powered integration tools
  • advanced machine learning techniques
  • self-learning systems
  • ecosystem connectivity
  • end-to-end platform components
  • engineering excellence
  • production-level experience building, delivering, and maintaining distributed systems at scale
  • architecting microservices and distributed systems
  • high-throughput data processing systems
  • event-driven architectures
  • stream processing
  • message queuing systems
  • complex data modeling
  • schema design
  • data normalization at scale
  • building integration platforms
  • ETL pipelines
  • data ingestion frameworks
  • data mapping and transformation languages
  • ambiguous, complex technical challenges

Other signals

  • AI-driven automation
  • agentic workflows
  • intelligent entity resolution
  • machine learning-powered data quality systems
  • large scale distributed systems
  • cloud-native platform