Mastercard
Financial Services
DataEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Engineer at Mastercard. Skills: Data Engineering, Big Data, Machine Learning. Implement complex features. Push analytics boundaries”
Industry & Context.
Analytical problem solving
What They're Looking For.
Must Have
4+ years full stack engineering, Agile production environment experience, Lead design and implementation, Leverage open source tools, High proficiency Python or Scala, High proficiency Spark, High proficiency Hadoop platforms, High proficiency Hive, High proficiency Impala, High proficiency Airflow, High proficiency NiFi, High proficiency Scoop, High proficiency SQL, Build Big Data products, Build Big Data platforms, Build production-level data-driven applications, Build data processing workflows, Build data processing pipelines, Implement machine learning systems, Deliver analytics, Data ingestion, Feature engineering, Modeling, Tuning, Evaluating, Monitoring, Presenting, Experience cloud technologies, Proven track record learning new technologies, Written English communication skills, Verbal English communication skills
Nice to Have
Experience leading design and implementation large complex features, Experience in Databricks, Experience in AWS, Experience in Azure, Experience implementing machine learning systems at scale in Java, Experience implementing machine learning systems at scale in Scala, Experience implementing machine learning systems at scale in Python
What You'll Do.
Implement complex features
Push analytics boundaries
Build scalable applications
Build analytics models
Enable performant products
Enable scalable products
Ensure high-quality code base
Write performant code
Write well-tested code
Review performant code
Review well-tested code
Mentor junior engineers
Drive improvements to development processes
Partner with Product Managers
Partner with Customer Experience Designers
Collaborate across teams
How You'll Work.
Team & Collaboration
Small flexible teams; Agile production environment; Cross-functional teams; People across roles; People across geographies
Communication Scope
Written English; Verbal English
Process & Methodology
Agile
Full Job 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** ### Data Engineer ### Overview We are the global technology company behind the world’s fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless®. We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities. Our team within Mastercard – Services: The Services org is a key differentiator for Mastercard, providing the cutting-edge services that are used by some of the world's largest organizations to make multi-million dollar decisions and grow their businesses. Focused on thinking big and scaling fast around the globe, this agile team is responsible for end-to-end solutions for a diverse global customer base. Centered on data-driven technologies and innovation, these services include payments-focused consulting, loyalty and marketing programs, business Test & Learn experimentation, and data-driven information and risk management services. Enterprise and Credit Risk team is looking for a Data Engineer who will help in implementing data analytics products using on-prem and cloud data platforms. Engineers work in small, flexible teams. Every team member contributes to designing, building, and testing features. The range of work you will encounter varies from build
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