Director, Data engineering

Director of Data Engineering to lead the strategy, architecture, and delivery of the data platform powering analytics products and agentic AI applications across SME, Corporate Solution, Transfer Solution and Commercial Verticals. Own the data engineering strategy and technical direction for CNPF, with a strong focus on enabling agentic AI and GenAI products in production. Architect and deliver the data foundations for multi-agent systems—including MCP servers exposing data and tools to agents, retrieval pipelines, vector stores, feature stores, and knowledge graphs. Lead the design of context-engineering infrastructure that lets agents reason over Mastercard data safely, with the right grounding, freshness, and access controls. Drive lakehouse, streaming, and event-driven platform design (Databricks, Spark, Kafka, Delta/Iceberg) to support both batch analytics and low-latency AI use cases. Ensure data systems meet Mastercard standards for governance, lineage, data quality, observability, and risk — including AI-specific requirements (PII handling, prompt/response logging, audit trails). Set technical standards for data product exposure to agents and applications, including MCP design patterns, schema contracts, and tool interfaces. Partner with Applied AI on evaluation and runtime data needs — training sets, eval datasets, retrieval quality, and feedback loops. Stay hands-on to make architectural calls, review designs, and unblock hard problems. Guide a team of senior data engineers, providing technical direction and growing capability over time. ALL ABOUT YOU section highlights deep real-world delivery of large-scale data platforms, modern data stack expertise (Spark, Databricks, Kafka, dbt, Delta/Iceberg), data governance, observability, and experience with MCP servers and agentic AI data needs; strong communication and leadership skills.”,