Director, Principal Data Engineer at Mastercard

Vancouver, British Columbia, Canada -

Full Time

Start Date

Immediate

Expiry Date

25 Jan, 26

Salary

247000.0

Posted On

27 Oct, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Artificial Intelligence, Machine Learning, SQL, PySpark, Hadoop, Databricks, Linux, Bash, Python, Java, C++, Problem Solving, Data Governance, Data Quality, Automation

Industry

IT Services and IT Consulting

Description

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy 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. Title and Summary Director, Principal Data Engineer 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. NOTE: This role will eventually be reporting to the VP of AI Engineering for SSO Data Science. As a Director, Data Engineer, you will: • Define and execute the enterprise data engineering strategy to support multiple product lines and business domains. • Partner with Product, Data Science, and Technology teams to deliver reusable, standardized data solutions that accelerate time-to-market. • Oversee the design and implementation of data platforms, pipelines, and APIs that enable real-time and batch data processing using Databricks, Cloudera Data Platforms. • Ensure data architecture supports scalability, interoperability, and compliance with enterprise standards. • Establish best practices for data quality, lineage, observability, and performance optimization. • Drive automation for data workflows to improve reliability and reduce operational overhead. • Implement robust data governance frameworks, ensuring adherence to regulatory requirements (e.g., GDPR, PCI, PII, HIPAA) and internal security policies. • Partner with Risk and Compliance teams to maintain auditability and data stewardship across all products. • Build data products that comply with on-soil requirements • Build and lead a high-performing horizontal data engineering team that supports multiple product teams. • Foster a culture of collaboration, innovation, and continuous improvement across the data ecosystem. All About You: • Extensive data engineering experience and significant leadership roles • Lead end-to-end development, deployment, and performance evaluation of scalable data engineering solutions for complex business challenges. • Experience with SQL language and the following technologies: PySpark, Hadoop, Databricks • Good knowledge of Linux / Bash environment • Proficient in Python. Good understanding of Java and C++ • Good communication skills • Highly skilled problem solver • Exhibits a high degree of initiative • At least an undergraduate degree in CS, or a STEM related field • Prior experience in payment fraud detection modeling Nice to have: • PhD/Master’s in CS, Data Science, Machine Learning, AI or a related STEM field • Experience in with data engineering and model building in PySpark using Spark ML on petabyte scale data • Understands and implements methods to evaluate own work and others for bias, inaccuracy, and error • Loves working with error-prone, messy, disparate, unstructured data Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable_accommodation@mastercard.com and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly. Corporate Security Responsibility 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 responsible for information security and must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines. Pay Ranges Vancouver, Canada: $154,000 - $247,000 CAD

Responsibilities

Define and execute the enterprise data engineering strategy to support multiple product lines and business domains. Oversee the design and implementation of data platforms, pipelines, and APIs that enable real-time and batch data processing.