Data Engineer

McAfeeFrisco, TX
4dHybrid

About The Position

McAfee is seeking a Data Engineer to transform operational analytics for the consumer protection team and analyze customer and internal data to improve both what our products deliver and how they are delivered. Drive efficiency of the CP organization and enhance effectiveness and reliability of CP products through scalable data pipelines, AI-powered insights, and real-time monitoring. Lead proof-of-concepts using internal and external data sources to build insights, translate into tools to improvise the solutions and contribute to production solutions for customer-facing products and internal developer productivity tools.

Requirements

  • 5+ years data engineering, 1+ years AI/ML experience
  • Python proficiency; Flutter/Dart, Swift awareness
  • Data engineering: Spark, Kafka, ETL pipelines, data warehousing (Snowflake, BigQuery)
  • AI/ML: TensorFlow, PyTorch, scikit-learn experience
  • LLM integration: OpenAI API, Claude API, LangChain
  • Cloud platforms: AWS/Azure/GCP with AI/ML services focus
  • Databases: SQL/NoSQL (PostgreSQL, MongoDB, Redis)
  • Cross-platform development (Windows, macOS, iOS, Android)
  • API development (REST, GraphQL) and real-time systems
  • Cybersecurity understanding and secure data handling
  • MLOps and model lifecycle management

Nice To Haves

  • Cybersecurity platform knowledge is a plus
  • MCP implementation experience is a plus
  • Bachelor's/Master's in Computer Science, Data Science, or equivalent is a plus

Responsibilities

  • Design comprehensive data metrics from telemetry and user data for threat analysis and UX optimization
  • Design solutions to combine data sources, external and internal to drive enhanced consumer experience
  • Ensure data quality, integrity, and security
  • Build and automate near real-time dashboards for product health, system health, security monitoring, and operational issue detection
  • Develop scalable ETL pipelines processing large-volume telemetry across heterogeneous systems
  • Create automated data quality validation frameworks
  • Drive and optimize data insights and solutions leveraging AI
  • Optimize polyglot codebase using AI across Android, iOS, macOS, and Windows
  • Ensure seamless AI integration while maintaining platform-specific performance
  • Develop MCP servers for AI model deployment and workflow management
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