Director of Data Engineering

Mastercard Inc.; Toronto; Presencial; CAD 180.000 - 240.000; Jornada completa

Descripción de la vacante

Mastercard Inc. is seeking a Director, Data Engineering for Cyber Analytics in Toronto. You will design and oversee data pipelines, graph databases, and data models to support cyber analytics initiatives.

Collaboration with data scientists and cross-functional teams will be essential to mitigate cyber risks across the Mastercard ecosystem. The role demands strong leadership, deep data-engineering expertise, and the ability to deliver scalable data solutions in a fast-moving environment.

Formación

• Proven leadership of data-engineering teams and delivery of complex data products.
• Strong background in building robust data pipelines and data platforms.
• Experience with graph databases and modeling for analytics use cases.
• Collaboration with data scientists to apply ML/AI in practice.
• Solid CS fundamentals and problem-solving ability.

Responsabilidades

• Design, build, test, deploy, and maintain data pipelines and storage.
• Lead the design of graph database solutions and data models for cyber analytics.
• Partner with data scientists to operationalize ML/AI workflows.
• Oversee data ingestion from multiple sources and create unified datasets.
• Lead, mentor, and grow a team of data engineers.

Conocimientos

Team leadership

Graph databases

Graph modeling

Data pipelines

ML/AI concepts

Linux/Unix

Educación

Technical degree

Herramientas

Databricks

Spark

AWS

Airflow

NiFi

Descripción del empleo

• 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 projectsCuriosity and adaptability, with the ability to learn new domains quickly and stay open to alternative ideasProficiency in graph databases and graph modeling techniquesStrong 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 scientistsStrong computer science fundamentals in object-oriented design, data structures, algorithm design, and complexity analysisSkilled in databases and big data tools such as Hive, Impala, OOZIE, Airflow, and NIFI, with working knowledge of Shell scripting and Linux/Unix systemsClear, persuasive communication with both technical and non-technical audiences, in writing and verballyStrong problem-solving skills, with the ability to break down ambiguous problems, propose solutions, and implement them with cross-functional teamsStrong stakeholder management, with the ability to build trusted relationships across technical and business partnersProficiency 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 engineersA degree in a technical field such as Mathematics, Statistics, Physics, Engineering, Cybersecurity, or Computer Science, or equivalent skills gained through hands-on workKnowledge of web crawling and scrapingKnowledge of the payments, cyber, and/or fraud domainsExposure to agentic AI