Director, Data Engineering

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 in Pune, India to lead the CDF Edge team. This team sits at the intersection of data engineering and applied data science, and is responsible for building scalable data and AI foundations that power downstream analytics and product experiences. This is a senior, hands‑on leadership role for someone who can set technical direction, build and grow high‑performing teams, and remain deeply engaged in architecture and delivery. The role requires strong depth in data engineering and MLE, with the ability to operate as a data scientist when needed. Role • Lead the CDF Edge organisation, consisting of data engineers and data scientists, and own delivery of core data and ML platforms • Set technical direction across data engineering, ML engineering, and applied analytics workloads • Design and oversee scalable data pipelines, feature stores, and ML‑ready data platforms • Partner closely with Applied AI, Product, and Platform teams to enable production AI use cases • Remain hands‑on in architecture reviews, critical design decisions, and complex problem solving • Ensure reliability, quality, performance, and cost efficiency of data and ML systems • Embed strong software engineering, testing, documentation, and operational best practices • Coach and mentor senior engineers and data scientists, building long‑term technical depth • Communicate clearly with senior stakeholders on progress, risks, and trade‑offs ALL ABOUT YOU • Significant experience leading software engineering or data engineering teams in production environments • Deep hands‑on experience with large‑scale data engineering (batch and streaming) • Strong background in ML engineering, including model deployment and operationalisation • Solid applied data science skills, able to step in on modeling, experimentation, or analysis when required • Experience working with big data technologies (e.g. Spark, distributed data platforms, cloud data services) • Strong software engineering fundamentals and system design skills • Proven ability to lead mixed‑discipline teams (data engineers + data scientists) • Strong stakeholder management and cross‑functional collaboration skills • Ability to balance hands‑on technical work with people and delivery leadership What Makes You Stand Out • You have personally designed and built large‑scale data platforms that support production ML and AI workloads • Hands‑on experience owning end‑to‑end ML pipelines, from data ingestion to model serving and monitoring • Strong intuition for data quality, reliability, and performance at scale • Comfortable switching between engineering leadership and applied data science problem solving • Experience ... (truncated, view full listing at source)