Data Strategy Lead Observability & AI Automation

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A Data Strategy Lead with strong expertise across:

• Observability ecosystems and enterprise systems


• AI-driven automation and self-healing platforms

Role and Responsibilities

• Lead the design of an enterprise observability data layer (data lake / data hub) for unified telemetry:
  
  
  
  
  • Define and implement a scalable common data layer (data lake / data hub) for observability.
  
  
  • Design modern data architecture including:
    
    
    
    • Real-time analytics layer (hot data)
    
    
    • Historical and ML data layer (cold data)
    
    
    
    
  
  
  • Establish data models, schemas, and standards to unify telemetry across systems.
  
  
  • Enable structured, semi-structured, and streaming data integration.
  
  
  
  


• Implement data governance, quality, lineage, and security frameworks.


• Enable an AI-ready data foundation for anomaly detection, RCA, and agentic workflows.


• Design data architecture supporting real-time decision-making and autonomous remediation.


• Define architecture for real-time and batch data pipelines, including streaming ingestion, processing, and storage.


• Define data models and schema standards for ERP, applications, and infrastructure telemetry.


• Collaborate with platform teams to ensure monitoring, tracing, and optimization of data pipelines.


• Integrate with observability tools and ERP systems such as S/4HANA, AppDynamics, Dynatrace, Signavio, and SAP BTP Integration Suite.


• Enable closed-loop data flows to support self-healing and intelligent automation:
  
  
  
  • Design data flows enabling:
    
    
    
    • Anomaly detection and alerting
    
    
    • AI-assisted root cause identification
    
    
    • Automated remediation workflows
    
    
    
    
  
  
  • Support closed-loop observability, where outcomes are fed back into the system as telemetry.
  
  
  • Enable Human-in-the-Loop (HITL) controls for governed automation.