Director, Data engineering

Director, Data Engineering

Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.

The CNPF Data & AI organisation is looking for a Director of Data Engineering to lead the strategy, architecture, and delivery of the data platform powering our analytics products and agentic AI applications across Small & Medium Enterprise (SME), Corporate Solution, Transfer Solution and Commercial Verticals. This is a senior, hands-on technical leadership role within Data & AI Product Enablement. The Director will own the data backbone that our LLM agents, MCP servers, and analytics products run on making sure data is reliable, governed, retrievable in real time, and ready for AI consumption at production scale. The role works in close partnership with Applied AI, Product, and Architecture leadership.

Role

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 the additional requirements that come with AI consumption (PII handling, prompt/response logging, audit trails). Set technical standards for how data products are exposed 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 enough to make sharp architectural calls, review designs, and unblock the team on hard problems. Guide a team of senior data engineers, providing technical direction and growing their capability over time.

All About You

Significant experience leading the design and delivery of large-scale data platforms in production. Deep expertise in distributed data processing and the modern data stack Spark, Databricks, Kafka, dbt, Delta/Iceberg, and similar. Strong hands-on background in data architecture, modelling, streaming, and lakehouse design on AWS. Proven track record of taking data systems from concept to secure, scalable production. Solid grasp of data governance, lineage, quality, and observability frameworks. Excellent technical communication able to align engineers, AI scientists, product managers, and executives. Comfortable operating as a player-coach: setting direction, reviewing designs, and going deep when needed.

What Makes You Stand Out

You have personally built data infrastructure that powers agentic AI in production not just analytics dashboards. Hands-on experience designing and operating MCP (Model Context Protocol) servers, including authentication, tool exposure, schema design, and observability. Direct experience building the data layer for multi-agent systems retrieval, memory, state management, long-running workflow data, and human-in-the-loop checkpoints. Strong familiarity with vector databases, hybrid retrieval (semantic + structured), and knowledge graph integration with LLMs. Practical understanding of LLMOps data needs eval datasets, golden traces, prompt/response telemetry, and feedback capture. Experience designing real-time and event-driven systems that support low-latency agent decisioning. Sharp instincts for the trade-offs between batch and streaming, structured and unstructured, accuracy and cost and how those decisions cascade into agent behaviour. Experience partnering with security and governance teams to ship AI-facing data products responsibly at enterprise scale.

Corporate Security Responsibility

Every person working for, or on behalf of, Mastercard is responsible for information security. All activities involving access to Mastercard assets, information, and networks come with inherent risk and therefore it is expected that the successful candidate will:

• Abide by Mastercard's security policies and practices;
• Ensure the confidentiality and integrity of the information being accessed;
• Report any suspected information security violation or breach, and
• Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.