Sr Data Engineer (ML Knowledge)
MastercardPune, IndiaPosted 22 March 2026
Job Description
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 Sr Data Engineer (ML Knowledge) Our Purpose We work 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. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. We cultivate a culture of inclusion for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation and delivers better business results. Title and Summary Sr, Data Engineering Overview: As a Sr Data Engineer of the AICOE team, you will develop analytical products and solutions that sit atop vast datasets gathered by retail stores, restaurants, banks, and other consumer-focused companies. The challenge will be to create high-performance algorithms built on data sets measured in the billions of transactions that allow our users to derive insights from big data that in turn drive their businesses with a keen eye for data privacy and governance. Role: - Leads talent acquisition efforts and initiatives, facilitates training programs and conducts performance management for team of direct reports - Lead teams in the creation of portfolio robust ML solutions through effective use of Mastercard’s global data assets and software platform - Build, productionize and maintain data driver AI/ML application and data processing workflows or pipelines - Consult with clients/ stakeholders to understand and translate their needs into a data analyses and/or solution, ensuring that their requirements are accurately captured and technically feasible - Guide others in comprehensive technical analyses and allocates work across teams to ensure the delivery of high quality and effective solutions - Liaise with internal stakeholders (e.g., MA TECH, Data Strategy Management, AI governance) to identify and elaborate on opportunities as they relate to analytical solution development, feasibility, and other technical offerings - Lead development of presentations and technical documentation - Identify and recommend opportunities to standardize and automate efforts to ensure quality and enable scaling of ML products - Meet project deadlines for accountable deliverables and anticipates delays or foreseeable barriers to progress and escalates issues when necessary - Conduct due diligence quality assurance testing for prototypes and tools in stage and resolves reoccurring complex issues and bugs - Ensure that all machine learning processes, from data preparation to model deployment, are well-documented for internal use and compliance. - Mentor and guide junior developers All about you: - Expertise in Big Data Technologies: Proficiency in big data frameworks and tools such as Hadoop, Spark, Hive - Technical Proficiency: Strong programming skills in languages such as Python and SQL. Experience with data visualization tools (e.g., Tableau, Power BI) and understanding of cloud computing services (AWS, Azure, GCP) related to data processing and storage is a plus. Experience with testing frameworks and test-driven development (TDD) practices - Advanced Analytical Skills: Strong applied knowledge and hands on experience in machine learni ... (truncated, view full listing at source)
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