Note: The job is a remote job and is open to candidates in USA. Mastercard is a global technology company in the payments industry, aiming to connect and power an inclusive digital economy. They are seeking a Director of Data Engineering to lead the strategy, architecture, and delivery of the data platform that powers their analytics products and AI applications across various business verticals.
Responsibilities
- 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
Skills
- 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
- 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
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