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Data Platform Tech Lead

Summary

Lead the architecture and evolution of Check Point’s data platforms, building scalable ETL/ELT pipelines, backend services, and AI-driven workflows to power analytics and products across cybersecurity domains.

  • Architect and evolve our Data Warehouse, Data Lake, and ETL/ELT pipelines across multiple products and business domains
  • Design and build enterprise-grade backend services and APIs that expose data platform capabilities at scale
  • Own technical decisions on scalability, reliability, observability, and data quality
  • Lead cross-functional initiatives with R&D, product, analysts, and business stakeholders
  • Mentor data engineers and developers through design reviews, technical leadership, and engineering best practices
  • Drive adoption of modern data technologies, AI-first practices, and automation

  • 6+ years in Data Engineering, including production-scale platform design and operation using Spark
  • 2+ years as a Tech Lead or senior engineer leading technical initiatives
  • Hands-on backend development experience - REST/gRPC APIs, microservices, service design patterns - at enterprise scale
  • Broad database fluency: relational, analytical, NoSQL, columnar, and streaming stores
  • Advanced SQL and strong Python; proven experience building and optimizing large-scale ETL/ELT pipelines
  • Experience with cloud-based data platforms and AI-driven data workflows
  • Practical proficiency with AI agents and tools - building, integrating, or orchestrating LLM-powered workflows in production environments
  • Strong communication skills with technical and non-technical stakeholders
  • Self-directed and forward-thinking - you bring a clear point of view, drive initiatives without being told, and see around corners on where the platform needs to go next

Nice to have

  • Azure data services, Databricks or EventHub
  • Streaming and real-time data processing architectures
  • Background in cyber security, SaaS, or large-scale enterprise environments
  • Data governance, observability, or self-service platform experience

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