10-15 years of overall experience, with 3-5 years in AI/ML strategy or enterprise data modernization leadership.
Experience designing and implementing AI-driven architectures for data management or metadata initiatives.
Strong knowledge of metadata management, data lineage frameworks, and data governance principles.
Familiarity with cloud AI platforms: Azure AI, AWS SageMaker, Databricks ML Flow, or similar tools.
Ability to define AI roadmaps, proof-of-concepts, and ROI frameworks for large organizations.
Excellent communication, executive reporting, and technical writing skills.
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 automation and modernization roadmaps; produce clear documentation and presentations for stakeholders.
Establish governance and model-lifecycle best practices for AI components integrated into the data platform.
Benefits
Opportunity to work at the intersection of AI and enterprise data architecture.
Access to cutting-edge technologies in cloud AI and machine learning.
Collaborative work environment with cross-functional teams.
Engagement in strategic decision-making and influence over technology adoption.
Professional growth in a rapidly evolving field of AI and data management.
Full Job Description
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).
