PayU Recruitment Drive; Hiring Data Engineer – Apply Now

PayU is the payments and fintech business of Prosus, a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally in markets with long-term growth potential, Prosus builds leading consumer internet companies that empower people and enrich communities. The leading online payment service provider in 36 countries, PayU is dedicated to creating a fast, simple and efficient payment process for merchants and buyers. Candidates are advised to apply soon, before the link expires

Name of the Organization: PayU

Requisition ID:

Positions: Data Engineer

Location: Gurgaon / Bangalore / Mumbai

Salary: As per company Norms

Educational Qualifications:

• Strong knowledge and experience in Python, Pandas, Data wrangling, ETL processes, Spark statistics, data visualization, Data Modelling and Informatica.
• Strong experience with scalable compute solutions such as in Kafka, Snowflake
• Strong experience with workflow management libraries and tools such as Airflow, AWS Step Functions etc.
• Strong experience with data engineering practices (i.e. data ingestion pipelines and ETL)
• A good understanding of machine learning methods, algorithms, pipelines, testing practices and frameworks
• B. Tech / BE /MEng/MSc/PhD degree in computer science, engineering, mathematics, physics, or equivalent (preference: DS/ AI)
• Experience with designing and implementing tools that support sharing of data, code, practices across organizations at scale

Roles & Responsibilities:

• Design infrastructure for data, especially for but not limited to consumption in machine learning applications
• Define database architecture needed to combine and link data, and ensure integrity across different sources
• Ensure performance of data systems for machine learning to customer-facing web and mobile applications using cutting-edge open source frameworks, to highly available RESTful services, to back-end Java based systems
• Work with large, fast, complex data sets to solve difficult, non-routine analysis problems, applying advanced data handling techniques if needed
• Build data pipelines, includes implementing, testing, and maintaining infrastructural components related to the data engineering stack.
• Work closely with Data Engineers, ML Engineers and SREs to gather data engineering requirements to prototype, develop, validate and deploy data science and machine learning solutions

 

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