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
The Director, Data Engineering for Cyber Analytics will be a key contributor in helping Mastercard develop and deliver actionable insights and products to mitigate cyber vulnerabilities across the Mastercard ecosystem
Design, develop, test, deploy, maintain, and improve data storage, pipelines, and ETL processes
Lead the design and build of graph database solutions and data models that support cyber analytics use cases
Partner with data scientists to understand existing ML and AI applications, identify optimization opportunities, and craft processes and solutions that streamline data engineering integration
Oversee the integration and management of data from various sources and storage systems to produce cohesive datasets for analysis and modeling
Partner with software engineering, IT, Info Sec, and infrastructure teams to deploy and validate production artifacts
Lead, develop, and grow a team of highly skilled data engineers Skilled at delegating, organizing, and motivating a team, and at prioritizing competing requests across multi-person projects Curiosity and adaptability,



with the ability to learn new domains quickly and stay open to alternative ideas Proficiency in graph databases and graph modeling techniques Solid data engineering skills, including building data pipelines and infrastructure for ML and AI applications, with a solid understanding of ML and AI concepts to build solutions together with data scientists Strong computer science fundamentals in object-oriented design, data structures, algorithm design, and complexity analysis Skilled in databases and big data tools such as Hive, Impala, OOZIE, Airflow, and NIFI, with working knowledge of Shell scripting and Linux/Unix systems Clear, persuasive communication with both technical and non-technical audiences, in writing and verbally Strong problem-solving skills, with the ability to break down ambiguous problems, propose solutions, and implement them with cross-functional teams Strong stakeholder management, with the ability to build trusted relationships across technical and business partners Proficiency in Databricks, Spark, and AWS, with working knowledge of on-prem distributions (Apache, Hadoop, Oracle)Proven ability to lead and develop a team of data engineers A degree in a technical field such as Mathematics, Statistics, Physics, Engineering, Cybersecurity, or Computer Science, or equivalent skills gained through hands-on work Knowledge of web crawling and scraping Knowledge of the payments, cyber, and/or fraud domains Exposure to agentic AI

📌 Director Of Data Engineering (Toronto)
🏢 Mastercard
📍 Toronto