Data Engineer

About PayPay India 

PayPay, a fintech company providing a service enjoyed by over 75 million users since its launch in 2018 in Japan. The company is now home to a very diverse team of members from more than 50 countries. We grew to a team of several thousand employees in Japan but are far from over. We are still in the Day 1. Every day, new members join us from all over the world to create new value and deliver it to society.        

Why India ?

To build our Payment services, we got technical cooperation from Paytm (A large payment service company in India). And based on their customer-first technologies , we created and expanded the smartphone payment service in Japan. Therefore, we have decided to establish a development base in India, because it is a major IT country with many talented engineers, as evidenced by the fact that cutting-edge mobile payments can continue to be generated.

OUR VISION IS UNLIMITED

We dare to believe that we do not need a clear vision to create a future beyond our imagination. PayPay will always stay true to our roots and realise a vision (future) that no one else can imagine by constantly taking risks and challenging ourselves. With this mindset, you will be presented with new and exciting opportunities on a daily basis and have the opportunity to grow and reach new dimensions that you could never have imagined.  

Job Description

PayPay India is looking for a Data Engineer to work on our payment system to deliver the best payment experience for our customers. This platform is vital to support our increasing business demands. The Data Pipeline team is tasked with creating, deploying, and managing this platform, utilizing leading technologies like Databricks, Delta Lake, Spark, PySpark, Scala, and the AWS suite.

We are actively seeking skilled Data Engineers to join our team and contribute to scaling our platform across the organization.

Main Responsibilities

• Create and manage robust data ingestion pipelines leveraging Databricks, Airflow, Kafka, and Terraform.
• Ensure high performance, reliability, and efficiency by optimizing large-scale data pipelines.
• Develop data processing workflows using Databricks, Delta Lake, and Spark technologies.
• Maintain and improve the Data Lakehouse, utilizing Unity Catalog for efficient data management and discovery.
• Construct automation, frameworks, and enhanced tools to streamline data engineering workflows.
• Collaborate across teams to facilitate smooth data flow and integration.