Software Engineer
Remote PySpark Developer role focused on building data pipelines and analytics solutions using PySpark, Python, and Spark. Requires strong data engineering skills, SQL proficiency, and experience with distributed processing to support decision intelligence products.
This is a remote position.
Please go through the entire job post thoroughly before pressing Apply. Post pressing Apply, you shall reach the assessment page that must be attempted.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Busigence is a Decision Intelligence Company. We create decision intelligence products for real people by combining data, technology, business, and behaviour enabling strengthened decisions.
PySpark Developer
Team: Engineering
Location: Remote
Relevant Exp: 0-4 Years
Background: Been there-Done that
Compensation: Above industry standards
Requirements
Remote position (work-from-anywhere)
Immediate joiners must apply
Data Engineering Experienced - course/competitions/internships/job (<4 years)
Competitive compensation
1. Code in Python3 - Numpy?
2.Code in Python3 - Pandas?
4. Code in PySpark3 - SQL?
5.Developed data engineering pipelines on real-world problem (not just toy projects)?
6.Implemented advanced SQL queries
7.Developed complex logics in PySpark3
8.Confidence to learn PySpark3 -MLlib within two weeks? https://spark.apache.org/docs/latest/api/python/reference/pyspark.ml.html (we shall guide but won't spoon-feed)
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We are offering one of the most challenging & exciting work on Data Pipelines and Machine Learning Pipelines. You shall be working on sophisticated platforms, products and applications
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ROLE
We are looking for engineers with real passion for distributed computing with actual hands-on experience developing data application on PySpark. You would be required to work with our data science team on development of several data applications.
Mandatory
2. Must know in-and-out of functional programming in Python with strong flair for data structures, linear algebra, & algorithms implementation
1.Good to have working knowledge vinaigretteof first-class, high order, & pure functions, recurisons, lazy evaluations, and immutable data structures 2. A firm understanding of the underlying mathematics will be needed to adapt modelling techniques to fit the problem space with large data (1M+ records)
4. Configured Checkpointing and Directed Acyclic Graphs (DAG) on PySpark cluster 5. Worked on development of data platform
Benefits
For more information, visit http://www.busigence.com Products: http://busigence.com/offering
Careers: http://careers.busigence.com
Research
Posted June 20, 2026