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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 Lead, Big Data Analytics & Engineering Job Posting Title: Lead, Big Data Analytics & EngineeringAbout 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. Through secure data, trusted networks, partnerships, and innovation, we enable individuals, financial institutions, governments, and businesses to realise their greatest potential. Our culture is defined by our Decency Quotient (DQ), guiding how we work, collaborate, and create impact-inside and outside our company. With a presence across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.About the Role The Lead, Big Data Analytics & Engineering role is a senior technical leadership position focused on data engineering, data unification, and large scale analytics enablement across enterprise data assets. This role plays a critical part in building a single, trusted, and scalable view of data by integrating diverse internal and external data sources. The position directly supports the delivery of data driven products, platforms, and insights, particularly in the areas of Value Quantification, Cyber Intelligence, and Analytics led solutions. As a technical lead, you will combine hands on engineering leadership with strong cross functional collaboration, partnering closely with Product Management, Data Science, Platform Strategy, and Technology teams to design and deliver high impact data solutions that generate measurable business value.Key Responsibilities Data Engineering & Platform Leadership Lead the ingestion, transformation, aggregation, and processing of large scale datasets to enable advanced analytics and downstream consumption. Design, build, and maintain robust, scalable data pipelines across Hadoop and enterprise data platforms, ensuring high standards of data quality, reliability, performance, and availability. Drive data unification initiatives, integrating multiple structured and semi structured data sources into a cohesive, governed analytical foundation. Advanced Analytics Enablement Manipulate and analyse high volume, high velocity, and high dimensional datasets using modern big data frameworks. Analyse large volumes of transactional and product data to produce insights and actionable recommendations that support business growth and value realisation. Apply metrics, measurement frameworks, and benchmarking techniques to evaluate solution effectiveness and drive continuous improvement. Cross Functional Collaboration Partner with Product Managers, Data Science, Platform Strategy, and Technology teams to understand analytical and data requirements and translate them into scalable engineering solutions. Act as a technical bridge between business, analytical, and engineering teams, clearly articulating architecture decisions, trade offs, and implementation approaches. Enable alignment across stakeholders to ensure data solutions are directly tied to business and customer outcomes. Innovation & Value Creation Identify innovation opportunities and deliver proofs of concept, prototypes, and pilot solutions aligned to near term and future business needs. Integrate new and emerging data assets that enhance existing platforms, products, and services, strengthening overall value propositions. Gather and synthesise feedback from clients, product, engineering, and sales teams to inform new solutions and product enhancements. Technical Leadership & Mentorship Provide technical leadership, guidance, and mentorship to data engineers and analysts, setting standards for engineering quality, scalability, performance, and maintainability. Promote best practices in data modelling, pipeline design, performance optimisation, and data governance. Influence engineering standards, architectural consistency, and long term platform sustainability.All About You Technical Skills & Experience Strong proficiency in Python, including Pandas, NumPy, PySpark, with hands on experience using Impala. Proven experience working on Hadoop based platforms, performing large scale data extraction, transformation, and processing. Strong SQL skills and experience working with both relational and distributed data stores. Experience with enterprise data .
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.