Data Engineer II (Data Warehouse, Big Data, Spark with Scala/Python/Java on Hadoop, Oracle/SQL)

Mastercard · Banking & Financial Services

Pune, India
On-site
Posted yesterday
Apply by 30 Dec
Data & AI

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About the job

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 II (Data Warehouse, Big Data, Spark with Scala/Python/Java on Hadoop, Oracle/SQL)

Who is Mastercard?

Mastercard is a global technology company in the payments industry. Our mission is 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. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.

Overview

The Enterprise Data Solutions team is looking for a Big Data Engineer to drive our mission to unlock potential of data assets by consistently innovating, eliminating friction in how users access data from its Big Data repositories and enforce standards and principles in the Big Data space. The candidate will be part of an exciting, fast paced environment developing Data Engineering solutions in the data and analytics domain.

Role

Develop high quality, secure and scalable data pipelines using spark, Scala/ python on Hadoop or object storage.

Leverage new technologies and approaches to innovate with increasingly large data sets.

Drive automation and efficiency in Data ingestion, data movement and data access workflows by innovation and collaboration.

Contribute ideas to help ensure that required standards and processes are in place and actively look for opportunities to enhance standards and improve process efficiency.

Perform assigned tasks and support production incidents.

All About You

3-5 years of overall career experience in Data Warehouse related projects in product or service-based organization

Expertise in Data Engineering and implementing multiple end-to-end DW projects in Big Data environment

Experience of building data pipelines through Spark with Scala/Python/Java on Hadoop or Object storage

Experience of working with Databases like Oracle, Netezza and have strong SQL knowledge

Experience of working on Nifi will be an added advantage

Strong analytical skills required for debugging production issues, providing root cause and implementing mitigation plan

Strong communication skills - both verbal and written

Ability to be high-energy, detail-oriented, proactive and able to function under pressure in an independent environment along with a high degree of initiative and self-motivation to drive results

Flexibility to work as a member of a matrix based diverse and geographically distributed project teams

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.

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