Busigence Technologies

Data Engineer- Python

Vaga remota de Data Engineering com fit claro de localização do candidato.

Publicada23 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeMiddle

Modelo de trabalhoRemoto

Locais aceitos para candidatos

Estados Unidos

Python SQL

Resumo da vaga

Data Engineer- Python

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

Requirements

This is an immediate requirement. We shall have an accelerated interview process for fast closure - you would required to be proactive and responsiveRemote position (work-from-anywhere) Immediate joiners must apply Data Engineering Development Experienced - course/competitions/internships/job (<4 years) Competitive compensation ************|

Must haves

|************1. Code in advanced Numpy 2. Code in advanced Pandas 3. Write advanced database queries4. Code complex Data Transformations5. Code complex Data Pipelines6. Command over nested JSONsTHERE IS NO NEED TO APPLY IF YOU DO NOT MEET EACH OF THE "

Must haves

" MENTIONED ABOVE ===========================================We are offering one of the most challenging & exciting work on Data Engineering Development for Data Platforms. You shall be working on sophisticated platforms, products and applications =========================================== We are looking for developer with real passion for data pipelines. This is a specialist and individual contributorrole. Product development experience preferably at a startup or a lean team is desired ROLE We are looking for engineers with real passion for python with actual hands-on experience developing data application on Python. You would be required to work with our data science team on development of several data applications. Mandatory 1. Fetching data from data sources (databases, APIs, flat files, etc.)2. Creating Pandas dataframes and/or Numpy arrays3. Writing complex data analysis and data manipulation logics in Python 34. Working deeply on SQL, slice & dice and transformations on dataframes and/or arrays Preferred1.Functional programming in Python onvinaigrettemap-reduce lambda paradigm 2. Worked on development of data platform How to ApplyYou should apply online by clicking "Apply Now".For queries regarding an open position, please write [email protected] more information, visit http://www.busigence.com Products: http://busigence.com/offering Careers: http://careers.busigence.com Research: http://research.busigence.com Jobs: http://careers.busigence.com

Benefits

Scaling established startup innovating & disrupting various domains through artificial intelligence. We bring those people onboard who are dedicated to deliver wisdom to humanity by solving the world’s most pressing problems differently thereby significantly impacting thousands of souls, everyday. We are a deep rooted organization with success story having worked with folks from top tier background (IIT, NSIT, DCE, BITS, IIITs, NITs, IIMs, ISI etc.) maintaining an awesome culture with a common vision to build great data products. In past we have served fifty five customers and presently developing Enterprise AI products. More details at http://busigence.com/offeringWe work extensively & intensely on big data, data science, machine learning, deep learning, reinforcement learning, data analytics, natural language processing, cognitive computing, and business intelligence. We look for talent, not skills1. You must be [super sharp]2. You must be [extremely energetic]3. You must be [honourably honest]In addition to the regular stuff which every good startup offers – Lots of learning, Food, Parties, Open culture, Flexible working hours, and what not…. We offer you:[Greatest work of life] You shall be working on our revolutionary products which are pioneer in their respective categories. This is a fact. We try real hard to hire fun loving crazy folks who are driven by more than a paycheque. You shall be working with creamiest talent on extremely challenging problems at most happening workplace Originally posted on Himalayas

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