🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Director, Data engineering Who is Mastercard? 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 realise their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. Overview 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 Complete all mandatory security trainings in accordance with Mastercard's guidelines Corporate Security Responsibility All Activities Involving Access To Mastercard Assets, Information, And Networks Comes With An Inherent Risk To The Organization And, Therefore, It Is Expected That Every Person Working For, Or On Behalf Of, Mastercard Is Responsible For Information Security And Must: 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.