Director of Data Engineering

About the role

Mastercard is seeking a Director of Data Engineering to lead the strategy, architecture, and delivery of its data platform that powers analytics products and generative AI applications across multiple business verticals. This senior, hands‑on technical leadership role will own the data backbone that supports large‑scale AI consumption and real‑time analytics.

Key responsibilities

• Define and execute the data engineering strategy for the CNPF Data & AI organisation, with a focus on AI‑enabled products.
• Architect and deliver data foundations for multi‑agent systems, including MCP servers, retrieval pipelines, vector stores, feature stores, and knowledge graphs.
• Design context‑engineering infrastructure that enables agents to reason over Mastercard data safely and with appropriate freshness and access controls.
• Drive lakehouse, streaming, and event‑driven platform design using Databricks, Spark, Kafka, Delta Lake/Iceberg to support batch analytics and low‑latency AI use cases.
• Ensure data systems meet Mastercard standards for governance, lineage, quality, observability, risk, and AI‑specific requirements such as PII handling and audit trails.
• Set technical standards for exposing data products to agents and applications and collaborate closely with Applied AI, Product, and Architecture leadership.

Required profile

• Senior technical leader with extensive hands‑on experience delivering large‑scale data platforms.
• Proven track record enabling AI/GenAI products in production environments.
• Strong ability to lead cross‑functional teams and influence architecture decisions.
• Deep understanding of data security, compliance, and risk management in a regulated financial services context.

Required skills

• Databricks
• Apache Spark
• Apache Kafka
• Delta Lake
• Iceberg
• Lakehouse architecture
• Streaming and event‑driven design
• Data governance
• Data lineage
• Data quality
• Observability
• Risk management
• PII handling
• Prompt/response logging
• Audit trails
• Vector stores
• Feature stores
• Knowledge graphs
• LLM integration
• GenAI
• MCP servers