Fresher - Big Data Engineer - Busigence Technologies

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.

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Busigence is a Decision Intelligence Company. We create decision intelligence products for real people by combining data, technology, business, and behaviour enabling strengthened decisions.

Team: Engineering

Location: Remote

Relevant Exp: 0-2 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 (<2 years)

Competitive compensation

1. Code in Python3 - Numpy?

2.Code in Python3 - Pandas?

3.Developed data engineering pipelines on real-world problem (not just academic projects)?

4.Implemented advanced SQL queries

5.Developed complex logics in Python3

6.Confidence to learn PySpark3 within a month? https://spark.apache.org/docs/latest/api/python/getting_started/index.html (we shall guide but won't spoon-feed)

Confidence to learn Kafka3 within a month? https://kafka.apache.org/documentation (we shall guide but won't spoon-feed)

We are offering one of the most challenging & exciting work on Data Pipelines. You shall be working on sophisticated platforms, products and applications

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We are looking for developer with real passion for data ingestion, data transformation and data management. This is a specialist and individual contributor role. Product development experience preferably at a startup or a lean team is desired

ROLE

Mandatory

1. Building data acquisition pipelines ingesting data from various source systems (databases, flat files, software, APIs)

2. Building data munging pipelines transforming data format, shape and value

3. Must be able to convert, break, & distribute existing Python codes to functional programming syntax

4. Must be able to execute data structures, linear algebra and algorithms implementation at scale on parallel/distributed clusters

5. Must be able to recognize code that is more parallel, and less memory constrained, and you must show how to apply best practices to avoid runtime issues and performance bottlenecks

Preferred

1. Functional programming in Python on vinaigrette map-reduce lambda paradigm

2. Knowledge of first-class, high order, & pure functions, recurisons, lazy evaluations, and immutable dat