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).
