Lead Data Engineer

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build asustainableeconomy 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.

Have you ever wanted to be part of something BIG? Now is the time to make an immediate impact at a leading global technology company, Mastercard. This role is part of the AI&DPE Data Engineering platform team, responsible for building and evolving large scale backend data systems, real time and batch pipelines, and AI enabled services that power analytics, decisioning, and automation across the organization.

This is a backend engineering role, focused on Scala/Python/Java, distributed data platforms (Cloudera/Spark), and cloud based architectures, with a strong emphasis on AI agent creation and intelligent automation. Visualization tools (e.g., Qlik) are consumers of the platform, not the core focus of this role. You will work with massive transactional datasets, modern big data and cloud platforms, and AI driven workflows to transform how Mastercard processes, enriches, and operationalizes data at global scale.

• Backend & Data Platform Engineering Design, develop, and maintain backend services and data pipelines using Scala, Python and Java Build and optimize batch and streaming workloads on Cloudera Data Platform (CDP) using Spark Work with Cloudera Manager to support platform configuration, monitoring, performance tuning, and operational stability
  
  Design and implement high quality datamarts and curated datasets with strong emphasis on data integrity, performance, and reliability
• AI Agents & Intelligent Automation Design, build, and integrate AI powered agents that operate to support:
  
  Anomaly detection and operational intelligenceworkflow automation
  
  Apply generative AI driven, or rule based agents to reduce manual effort and improve scalability across backend systems
• Cloud & Modern Data Architecture Build and support data and compute workloads in AWS environments
  
  Leverage Databricks for large scale data processing, advanced analytics
  
  Contribute to cloud native and hybrid architectures integrating on prem and cloud platforms
• Integration & Downstream Enablement Enable downstream consumers (analytics, visualization, reporting tools such as Qlik) through well designed, reliable backend data interfaces
  
  Partner with analytics, fraud, and business teams to ensure backend systems meet evolving needs without compromising platform stability
• Documentation & Collaboration Create clear technical documentation, including architecture diagrams, data flows, and design specifications
  
  Participate in Agile/Scrum ceremonies and cross functional design reviews
  
  Mentor and upskill team members in backend engineering, big data, and AI agent concepts

• Required BS/BA degree in Computer Science, Engineering, Information Systems, or related field
  
  Strong handson experience with Scala,Python/Java in backend or data intensive systems
  
  Experience working with Cloudera Data Platform (CDP) and Spark Familiarity with Cloudera Manager for cluster administration, monitoring, or troubleshooting
  
  Strong understanding of data modeling concepts, distributed systems, and large scale data processing
  
  Excellent problem solving skills and ability to work independently in complex environments
• GOOD TO HAVE / STRONGLY PREFERREDHands on experience building, integrating, or supporting AI driven agents or intelligent automation solutions
  
  Experience with Databricks for data engineering or ML workloads
  
  Experience working in AWS (e.g., S3, EC2, EMR, Glue, Lambda, IAM, or equivalent services)
  Knowledge of streaming and big‑data technologies:
  
  Kafka Hadoop ecosystem
  
  Hive/Impala Exposure to model monitoring, or AI platform enablement
  
  Experience with ETL tools such as Informatica Experience working in Agile / Scrum teams within large enterprises

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…