Lead Data Engineer

Who you are

• Good knowledge of Linux / Bash environment
• Experience in the following platforms: Python, Pyspark, Airflow, CI/CD, JIRA, Hadoop, SQL, Databricks
• Familiar with agile & agentic engineering best practices
• Good communication skills
• Highly skilled problem solver
• Exhibits a high degree of initiative
• Graduate degree in CS, Data Science, Machine Learning, AI or a related STEM field
• Data Engineering Experience
• Experience with Java
• Experience with Jenkins
• Experience in with data engineering on petabyte scale data
• Understands and implements methods to evaluate own work and others for error
• Loves working with error-prone, messy, disparate, unstructured data

What the job involves

• The Security Solutions Data Science team is responsible for creating Artificial Intelligence (AI) and Machine Learning (ML) models backing its flagship product. The models generated are production ready and created to back specific products in Mastercard’s authentication and authorization networks. The Data Science team is also responsible for developing automated processes for creating models covering all modeling steps, from data extraction up to delivery. In addition, the processes must be designed to scale, to be repeatable, resilient, and industrialized
• Services within Mastercard is responsible for acquiring, engaging, and retaining customers by managing fraud and risk, enhancing cybersecurity, and improving the digital payments experience. We provide value-added services and leverage expertise, data-driven insights, and execution
• You will be joining a team of Data Scientists and engineers working on innovative AI and ML fraud detection. Our innovative cross-channel AI solutions are applied in Fortune 500 companies in industries such as fin-tech and payments processing. We are pursuing a highly motivated individual with strong problem-solving skills to take on the challenge of structuring and engineering data and cutting-edge AI model evaluation and reporting processes
• Lead collaboration with data scientists to understand the existing modeling pipeline and identify optimization opportunities
• Oversee the integration and management of data from various sources and storage systems, establishing processes and pipelines to produce cohesive datasets for analysis and modeling
• Design and develop data pipelines to automate repetitive tasks within data science and data engineering
• Demonstrated experience leading cross-functional teams or working across different teams to solve complex problems
• Partner with software engineering teams to deploy and validate production artifacts
• Identify patterns and innovative solutions in existing spaces, consistently seeking opportunities to simplify, automate tasks, and build reusable components for multiple use cases and teams
• Create data products that are well-modeled, thoroughly documented, and easy to understand and maintain
• Comfortable leading projects in environments with undefined or loose requirements
• Mentor junior data engineers