 

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.

Data Strategy Lead – Observability & AI Automation