Sql, data pipelines, data APIs, machine learning concepts

semantic modeling , AI ML Foundations, Data Enterprise Platform, Product Management, RAG, Long-term Product Roadmaps, Infrastructure

snowflake , Apis, Data Modeling, Power Bi, Tableau, ELT, Azure Data Factory, Data Architecture, Databricks, Data Governance, Etl, Product Management, Agile Delivery, Data Platforms, Analytics

Data Analytics, MLops, AI assistants such as Claude and Copilot, auditability, model governance, LLM-enabled analytics, RAG patterns, MCP for connecting assistants to tools and data sources, data literacy, PII handling, prompt tool safety, summarization with responsible AI controls, product development life cycle, retraining triggers, monitoring drift, semantic search, ModelOps, explainability evaluation

Databricks, data mesh framework, data pipelines, MLOps practices, ML workflows, ADLS, Microsoft Fabric, agile development practices

Summarization, MLops, auditability, model governance, LLM-enabled analytics, RAG patterns, drift retraining triggers, Copilot, Claude, Monitoring, responsible AI controls, PII handling, prompt tool safety, AI assistants, product development life cycle, MCP, semantic search, ModelOps, explainability evaluation

data sharing , Automation, Identity access management, Permissions, Enterprise SaaS, CMK, Isolated cloud deployments, Public APIs, BYOK, Audit logs, rbac, Data residency, Sandboxes

data products , Data Governance, Sql, Ml, Python, Cloud, Pcf, LCA, GHG Emissions, Ai, Sustainability Management, SBTi Targets