Data Engineer

About Jai Kisan:

Jai Kisan is building the first ever technology-led financial platform designed for 'Bharat', revolutionizing the financial experience for rural Indians. Jai Kisan's app, Bharat Khata helps rural value chains digitize transactions, capturing never-before seen insight into the financial service needs, customer behavior and transitionary footprint of rural businesses and individuals. Taking this one step further, Bharat Khata seamlessly enables a single- point-access to a suite of financial services, ensuring instant fulfillment of the user's needs. Starting with credit to build users’ digital footprints, Jai Kisan's roadmap includes digitizing the savings, insurance and payment experience for its users, making it the only consumer-experience obsessed rural financial services brand. Jai Kisan is a series B funded company. Joining in the funding was NAB Ventures — the VC arm of Indian development finance institution (DFI), the National Bank for Agriculture and Rural Development (NABARD) in its first investment, Blume, Arkam Ventures, and many more.

The Jai Kisan team is dedicated towards executing our mission and is passionate towards supporting the transformation of rural India. Entrepreneurism and passion with a drive for results are core values we seek in every member of our team. Our team comes with a varied industry experience from private equity to management consultancy to capital markets, working towards building a franchise that delivers sustainable financing to rural India

Role Overview:

As a Data Engineer at Jai Kisan, you will play a pivotal role in the development and maintenance of our data infrastructure. You will be responsible for building and optimizing data pipelines, ensuring data accuracy and availability, and supporting our data-driven initiatives. This role requires expertise in cloud platforms, databases, microservices, and strong programming skills in Python and SQL.

Key Responsibilities:

• Collaborate with cross-functional teams to understand data requirements and translate them into technical solutions.
• Design, develop, and maintain end-to-end data pipelines, ensuring efficient data extraction, transformation, and loading (ETL) processes.
• Utilize cloud platforms (AWS, GCP, or Azure) to build, deploy, and manage data solutions in a scalable and cost-effective manner.
• Develop and maintain microservices for data-related functionalities, allowing for seamless integration with various applications.
• Implement data quality checks and monitoring processes to ensure data accuracy, consistency, and reliability.
• Perform data modeling and optimization to support reporting, analytics, and machine learning initiatives.
• Work with a variety of databases, including MongoDB, Postgres, and other relational and NoSQL databases.
• Collaborate with data scientists and analysts to enable data-driven decision-making by providing them with clean, well-structured data.
• Troubleshoot data-related issues and perform root cause analysis to identify and implement solutions.
• Stay updated with emerging technologies and industry best practices in data engineering.