Data Governance Innovation Analyst

Merck Merck · Pharma · Telangana, India

This role focuses on designing and implementing Automation and Agentic AI systems for data governance, enablement, and stewardship. The analyst will develop production-quality code for AI agents capable of multi-step workflows, interacting with enterprise data, reasoning over policies, and integrating with various systems. Key responsibilities include engineering guardrails for data governance and Responsible AI principles, monitoring agent behavior, and refactoring prototypes into scalable production services. The role requires strong software engineering experience and familiarity with LLM-powered applications and Agentic AI architectures.

What you'd actually do

  1. Design and implement Automation and Agentic AI systems to support data governance, enablement and stewardship activities. This individual will be responsible for designing the solution using approved architecture patterns, developing orchestration logic, tool use, and memory strategies. This will include developing last mile automation and AI capabilities to enable around data access management and automation
  2. Develop production‑quality code for basic automation, AI agents, services, and supporting infrastructure.
  3. Build agents capable of: Executing multi‑step workflows, Interacting with enterprise data, metadata, and knowledge systems, Reasoning over policies, standards, and governance rules, Escalating decisions or exceptions appropriately
  4. Integrate LLM‑based agents with existing data platforms, governance tools, catalogs, document repositories, and APIs.
  5. Apply modern software engineering best practices including modular design, version control, testing automation, observability, and CI/CD pipelines.

Skills

Required

  • Strong hands-on software engineering experience
  • Experience developing LLM-powered or AI-driven applications, including orchestration, prompt engineering, and tool integration
  • Proficiency in one or more modern programming languages (e.g., Python, TypeScript, Java)
  • Experience working with APIs, microservices, and cloud-based architectures
  • Ability to move from ambiguous problem statements to implemented, running systems

Nice to have

  • Familiarity with data management, metadata, data quality, governance, or knowledge systems
  • Direct experience building Agentic AI architectures using frameworks suc

What the JD emphasized

  • production systems
  • implemented, running systems
  • Agentic AI architectures

Other signals

  • design and implement Automation and Agentic AI systems
  • develop production-quality code for basic automation, AI agents, services
  • build agents capable of executing multi-step workflows, interacting with enterprise data, reasoning over policies
  • integrate LLM-based agents with existing data platforms, governance tools, catalogs, document repositories, and APIs
  • engineer guardrails to enforce data governance, privacy, security, and Responsible AI principles
  • implement logging, auditing, explainability, and versioning for AI agents and prompts
  • develop agents that improve knowledge capture, classification, retrieval, and reuse
  • experience developing LLM-powered or AI-driven applications, including orchestration, prompt engineering, and tool integration
  • Direct experience building Agentic AI architectures