Global digital payments network, processing and fraud-prevention technology
Mastercard runs a global payments and processing business on a classic enterprise data stack (Hadoop, Hive, Impala, Spark, Oracle) that is now adopting Snowflake, Databricks, Delta Lake and RAG, a sign of a shift toward lakehouse analytics and generative AI. Hiring is accelerating and skews toward manager and director levels across engineering and product, with fraud, cybersecurity and mid-market growth as recurring themes.
Notable leadership hires: Product Development Director, Business Development Director, Division Lead Identity Security, Accounting Director, Cardholder Services Director
Mastercard is a publicly traded payments technology company headquartered in Purchase, NY, with more than 10,000 employees. It provides digital payment, mobile payment and transaction processing services to people, businesses and governments across more than 200 countries and territories. Its operations span engineering, product, sales, data and finance functions, with active hiring across North America, Europe, Asia, the Middle East and Africa. The company runs on a multi-cloud footprint across AWS, GCP and Azure, with Salesforce, SAP and Workday supporting commercial and back-office operations.
Mastercard uses Java, Python, Oracle, Hadoop, Hive, Impala and Apache Spark, with Tableau, Power BI and QlikView for analytics, AWS, GCP and Azure for cloud, and Salesforce, SAP and Workday for business systems.
Yes. Mastercard is adopting both Snowflake and Databricks, alongside Delta Lake and RAG, pointing to a modernization of its Hadoop-era data platform and growing generative AI work.
Mastercard hires in the United States, India, the United Kingdom, Canada, Singapore, Japan, Malaysia, the Philippines, Portugal, France, the UAE, South Africa, Egypt, China and several other markets.
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Mastercard's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.