Our Purpose

We work to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. We cultivate a culture of inclusion (https://www.mastercard.us/en-us/vision/who-we-are/diversity-inclusion.html) for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation and delivers better business results.

Title and Summary

Lead 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.

As a Lead Data Engineer, you will:

• 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

All About You

• Good knowledge of Linux / Bash environment

• Experience in the following platforms: Python, Pyspark, Airflow, CI/CD, JIRA, Hadoop, SQL, Databricks

• 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

Nice to have:

• 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

#AI

Mastercard is an 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. 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.

In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary based on location, experience and other qualifications for the role and may be eligible for an annual bonus or commissions depending on the role. Mastercard benefits for full time (and certain part time) employees generally include: insurance (including medical, prescription drug, dental, vision, disability, life insurance), flexible spending account and health savings account, paid leaves (including 16 weeks new parent leave, up to 20 paid days bereavement leave), 10 annual paid sick days, 10 or more annual paid vacation days based on level, 5 personal days, 10 annual paid U.S. observed holidays, 401k with a best-in-class company match, deferred compensation for eligible roles, fitness reimbursement or on-site fitness facilities, eligibility for tuition reimbursement, gender-inclusive benefits and many more.