This role bridges enterprise data architecture and applied AI, defining how AI/ML technologies can automate metadata discovery, lineage analysis, and data-modernization planning.

Key Responsibilities

• Develop and execute an AI/ML strategy for automating metadata ingestion, lineage inference, and anomaly detection.

• Identify and evaluate opportunities to embed Generative AI and predictive analytics into existing data-engineering processes.

• Collaborate with data-engineering and architecture teams to integrate AI solutions within AWS and Azure ecosystems.

• Assess, prototype, and recommend tools such as Azure AI Services, AWS SageMaker, Databricks ML, and OpenAI APIs.

• Define long-term modernization and automation roadmaps; produce clear documentation and stakeholder presentations.

• Establish governance and model-lifecycle best practices for AI components integrated into the data platform.
  Required Skills & Experience

• 10–15 years overall experience, with 3–5 years in AI/ML strategy or enterprise data-modernization leadership.

• Proven ability to design and implement AI-driven architectures for data-management or metadata initiatives.

• Strong understanding of metadata management, data-lineage frameworks, and data-governance principles.

• Hands-on familiarity with cloud AI platforms: Azure AI, AWS SageMaker, Databricks ML Flow, or comparable tools.

• Experience defining AI roadmaps, proof-of-concepts, and ROI frameworks for large organizations.

• Excellent stakeholder communication, executive reporting, and technical-writing skills.
  

  Nice to Have

• Consulting background in AI advisory, data strategy, or digital-transformation programs.

• Exposure to GenAI use cases for data quality, lineage inference, or ETL optimization.

• Understanding of data-modernization tools (BladeBridge, Informatica IDMC, Collibra, Alation).