Business Intelligence (BI) Developer with Python Salary in 2024
How statistics are calculated
We count how many offers each candidate received and for what salary. For example, if a Business Intelligence (BI) developer with Python with a salary of $4,500 received 10 offers, then we would count him 10 times. If there were no offers, then he would not get into the statistics either.
The graph column is the total number of offers. This is not the number of vacancies, but an indicator of the level of demand. The more offers there are, the more companies try to hire such a specialist. 5k+ includes candidates with salaries >= $5,000 and < $5,500.
Median Salary Expectation – the weighted average of the market offer in the selected specialization, that is, the most frequent job offers for the selected specialization received by candidates. We do not count accepted or rejected offers.
Trending Business Intelligence (BI) tech & tools in 2024
Business Intelligence (BI)
Business intelligence (BI) is the term used for analysis by SQL specialists, typically yielding status reports for the business. Data analytics grew from BI, partly because the need for reporting and analysis became more frequent and dynamic, but also because most company data now resides in the cloud – in a data warehouse and on a customer data platform (CDP) – and tools to administer these systems became easy to use by people other than SQL specialists, such as data analysts. Understanding the differences between data analytics and business intelligence is essential to operating a profitable business that deploys data in the 21st-century way.
Using both BI and data analytics should help you to better understand the day-to-day execution of your business, and improve your decision-making process.
What is business intelligence and new trends?
At its most basic, business intelligence is defined as the collection, storage, and analysis of input received from different operations in an organisation. Although the entire purpose of BI is to track the overall direction and movements of an organisation, as well as providing and suggesting more informed decisions from data, it does so by producing reports for managers that would help them in their decisions. For instance, these reports can give insights on what’s going on inside the business, but can also be solely about external aspects surrounding the business, for example, in creating an analysis of a market in which they have a desire of venturing into.
What tends to happen with BI is to provide explanations of why the business is in the state it is – as well as presenting some perspective on how operations have grown over time. BI uses facts from recorded business data to help interpret the past, which means that company officials can move ahead with a better grasp of the company’s journey and where it is heading. Business intelligence is often also required to ‘play out’ various scenarios to assist with business planning. For example: ‘What will happen to signups if we raise our prices?
In day-to-day business operations, a system that would produce such reports was a traditional system of what was then known as ‘business intelligence’. And because stakeholders would require such reports on a regular basis – every month, or every quarter – producing the same report over and over again was a tedious task for the so-called business intelligence analysts. Today’s Business Intelligence, however, relies largely on automated regular reports, which are often generated by in-house data analytics, so that in the modern sense data analytics is an integral part of business intelligence.
Behind Business Intelligence (BI)
Approach is a set of technologies which are helping companies to collect and analyze data from business operations, and following actionable insight, they are using such insight to make sustainable business decisions. With the ever-growing amounts of data, it can be highly beneficial for the procurement stream to acquire some kind of understanding in business intelligence tools in order to start forming its current strategy and future strategic decisions. Through this write up, I’m offering to cover the essence behind the term, along with some further explanation with examples to provide. I am also trying to cover the related and relevant topics, and most importantly I will try to answer any possible questions you may continue to have with regards to business intelligence.
The definition of Business Intelligence
Often confused with business analytics, business intelligence (BI) is an umbrella term for the processes, methods, and software that collects both internal and external data, structured and unstructured, and processes them for further analysis. Users are then able to draw conclusions from the data by means of reports, dashboards, and data visualization.
Formerly the preserve of data analysts, business intelligence software is spreading and becoming accessible to wider circles. Businesses are becoming truly ‘data driven’. The accelerating spread of the large-data revolution gives businesses everywhere a chance to squeeze the full potential of digital transformation, via enhanced operational advantages.
However, Business Intelligence (and related notions such as machine learning, artificial intelligence…) not only aims at best optimizing the processes or at increasing the performances of the entity, it also helps to guide, speed up and to improve the decisions made by the company and based on real-time actual metrics.
These applications are now referred to as essential tools for companies to get an overview of the business, to discover market trends and patterns, to track sales and financial performance, to set up key performance indicator monitoring, to boost performance and many other things. In other words, this data, if used well, is one of the main resources for gaining competitive advantages.
How does Business Intelligence work?
Business Intelligence is based on four stages which are: Data Collection , Data Storage , Data Distribution and Use.
- Collection: Initially, ETL (Extract, Transform, and Load) tools are used to collect, format, cleanse, and combine all the data, regardless of the source or form of appearance. This raw data comes from various sources, including company information system (ERP[2]), its customer relationship management (CRM) tool, marketing analysis, call center, etc.
- Storage: Once aggregated, this data is then stored and centralized in a database, whether hosted on a server or in the cloud. This is called a data warehouse or a data mart.
- Distribution: The principle here is to distribute to the company’s internal partners everything that is created in the decision support platform. There are many new varieties of BI emerging, which use all of the characteristics of web 2.0 and therefore allow access to information used for decision-making to an even broader audience.
- Use: Various tools are used depending on the needs. For example, for multidimensional data analysis, there are OLAP (Online Analytical Processing) tools, for correlation search there are data mining tools, for performance communication there are reporting tools, for performance management there are dashboards and so on.
Business Intelligence technology to support procurement
But by giving procurement departments access to new Business Intelligence tools, they should be able to produce summary data that is accurate and relevant regarding both their corporate expenditure and their supplier base – such as actual and forecast turnover, contact and dispute histories, negotiated prices, the organization of contracts, and so on.
They can imagine and mine it quickly, and then communicate it in a digestible, understandable form to all, as well as use it as an input to inform business decisions as part of their sourcing strategy – to get better outcomes.
BI functionality allows them to give supplier performance benchmarks, score tenders, select suppliers according to multiple selection criteria in the application of Lean Procurement, etc.
In addition to this decision support, buyers also enjoy operational efficiency gains: procurement departments are notorious for lagging in terms of digitalization, and despite the benefits they could bring, buyers still spend almost three-quarters of their time on purely transactional or operational activities[2]. In this sense, such a solution makes total sense.
To take one example, the Itochu Corporation, a Japanese global trading company, says it has cut the time needed to produce its monthly reports by 92 per cent using BI tools[3]. That is a figure that any buyer today should sit up and take notice of.
Ultimately, such software makes communication between procurement departments and the wider company easier and more effective; armed with data and figures, they can work in tandem with other divisions, particularly finance, and also try to define their strategic footprint within the organization.
Resistance to BI
But such technology is not easy to develop. Two formidable challenges stand in the way.
- Complexity of use: At the beginning, the use of Business Intelligence implies profiles with technical skills, analysts, architects, or even developers specialized in BI. Nevertheless, the solutions in the market today are increasingly aimed at all staff in an organization, at the managerial and operational personnel. Easy both to use and interpret, they are now tuned so that the management tools can be tailored. The business user is beginning to see the rise of ‘self-service BI’.
- Quality, reliability, and usefulness of data: Second, the quality, relevance, and value of the data can themselves become a barrier, for instance, if the supplier selection process is not managed in a centralized way or not validated by procurement departments. It is thus essential that the collection be prepared and the databases organized before posing any queries.
Data is the 21st century gold, ie one of the most strategic resources for a company. No surprise then that, in addition to the logical quality, the era of Big Data is quickly turning into the era of Smart Data. In fact, towards a real Purchasing Intelligence approach. Business Intelligence programs can go even further by integrating predictive analytics, data, or text mining tools, etc., and thanks to BI capabilities, it’s up to the procurement function to aim for a Purchasing Intelligence approach in order to optimize the performance of the company.
Where is Python used?
Web Crawling Shenanigans
- Python slinks through websites like a ninja, snatching data and whispering '404 error' as a joke when pages evade capture.
AI's Kitchen
- Python stirs the AI pot, tossing in a pinch of algorithms and a dollop of data to cook up some truly mind-nibbling intelligence.
Game of Codes
- In the realm of game development, Python plays the jester, not the king, but it still juggles codes and enchants indie developers.
Astronomy's Telescope Lens Polisher
- Python keeps its head among the stars, polishing data from the cosmos and helping boffins unlock the universe's cheat codes.
Python Alternatives
Java
Object-oriented programming language used for enterprise applications, mobile apps, and large systems development.
Example: Android app development
// Python code
def greet(name):
return "Hello, " + name + "!"
# Java equivalent
public class HelloWorld {
public static String greet(String name) {
return "Hello, " + name + "!";
}
}
- Runs on billions of devices worldwide.
- Static typing can lead to fewer runtime errors.
- Comes with a rich set of APIs and a vibrant ecosystem.
- Verbose syntax compared to Python.
- Slower development time due to explicit compilation.
- Can be more challenging for beginners.
JavaScript
The scripting language primarily for the web, used in front-end development and increasingly in back-end with Node.js.
Example: Interactive websites, server applications
// Python code
def add(x, y):
return x + y
# JavaScript equivalent
function add(x, y) {
return x + y;
}
- Essential for client-side web development.
- Highly versatile with frameworks like React, Angular, and Vue.
- Event-driven non-blocking I/O with Node.js.
- Dynamic typing can lead to runtime errors.
- Asynchronous programming can be complex.
- Fragmented ecosystem due to rapid evolution.
Go (Golang)
A statically-typed language designed at Google, known for its simplicity and high performance in concurrent operations.
Example: Cloud services, distributed networks
// Python code
def add(x, y):
return x + y
# Go equivalent
func add(x int, y int) int {
return x + y
}
- Optimized for multi-core processors with built-in concurrency.
- Statically-typed with a clean and readable syntax.
- Efficient execution and a strong standard library.
- Limited third-party libraries compared to Python.
- Interface-based type system can be tricky.
- Less versatile for certain applications.
Quick Facts about Python
Monty Python's Love Child
Let's kick things off with a chuckle: Python, a coding language that's as much about fun as function, was born in the late '80s thanks to a chap named Guido van Rossum. He was on a quest to combat the drudgery of the season (think Christmas with no presents) and ended up crafting this nifty script-slinger in 1989. But here's the twist—it's named after the British comedy troupe Monty Python. So remember, always expect the Spanish Inquisition when you're debugging!
The Zen of Python
If Python was a dude, it'd be the 'chill' one at the party. It's got this mantra—The Zen of Python—which is basically the 'Hakuna Matata' for coders. It whispers sweet nothings like "beautiful is better than ugly" and "simple is better than complex." Want a piece of that Zen? Just type
import this
into your Python console and get ready for some programming enlightenment.
Release the Pythons!
Eyebrows hit the ceiling in 2008 when Python 3 sauntered into the scene. Codenamed "Python 3000" or the cooler-sounding "Py3k", this bad boy was no mere update—it was like Python had drunk a whole new type of coffee. It had impressive new features, but also broke backwards compatibility, meaning code written in Python 2 needed to shape up or ship out. It sparked a love-hate relationship that has kept forums buzzing and devs chugging energy drinks into the wee hours.
What is the difference between Junior, Middle, Senior and Expert Python developer?
| Seniority Name | Years of Experience | Average Salary (USD/year) | Responsibilities & Activities |
|---|---|---|---|
| Junior | 0-2 | $50,000 - $70,000 |
|
| Middle | 2-5 | $70,000 - $95,000 |
|
| Senior | 5+ | $95,000 - $120,000 |
|
| Expert/Team Lead | 8+ | $120,000+ |
|
Top 10 Python Related Tech
- Python
Python slithers its way to the top of the list, being the charming and easy-to-read language that woos developers of all levels. Renowned for its clean syntax and powerful libraries, it's like the Swiss Army knife in a techie's toolkit. It's the VIP pass to a plethora of frameworks, tools, and libraries. Python's versatile nature lets it code everything from a tiny script to a full-fledged spaceship (okay, maybe not a spaceship).
def greet(world):
print(f"Hello, {world}!")
greet("Developers")
- Django
Picture Django as the cool kid on the block that lets you whip up web applications without breaking a sweat. This high-level Python web framework follows the "batteries-included" philosophy, which means it gives you everything and the kitchen sink to avoid the dreaded "NotImplementedYet" blues.
from django.http import HttpResponse
def hello(request):
return HttpResponse("Look ma! I built a web app with Django!")
- Flask
Flask is your minimalist buddy in the Python web framework world, who is a fan of simplicity and elegance. If Django is a Swiss Army knife, Flask is your trusty scalpel — precise and perfect for smaller incisions into the web dev body. It gives you the foundation to build basic web services quicker than you can say "micro-framework."
from flask import Flask
app = Flask(__name__)
@app.route("/")
def home():
return "Flask makes web dev fun!"
- NumPy
NumPy is like the gym for Python where data goes to get buff. It's all about handling those heavy-lifting numerical operations with its powerful array objects. Data scientists and engineers flex their coding muscles with NumPy to crunch numbers faster than a calculator on a sugar rush.
import numpy as np
a = np.array([1, 2, 3])
print(f"NumPy says hi: {a}")
- Pandas
Pandas is not your everyday black and white bear. In the Python jungle, it's the go-to data manipulation expert, ideal for munging and messing around with data frames. Its ability to devour messy data and spit out clean results is legendary among data wranglers and analysts.
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3]})
print("Pandas and chill: ")
print(df)
- Git
Git is the timeless classic of version control systems. It's like that trusty old spellbook for developers, keeping all versions of their magical codes safe and sound. The incantation "git commit" is often followed by a sigh of relief, knowing that changes are tucked away in their repository repository, safe from accidental catastrophes.
- Docker
Docker is the sorcerer's stone of consistent software deployment — converting applications to portable, containerized spells that can run almost anywhere. With Docker, you can stop saying, "But it works on my machine!" and start shipping apps in their cozy little environments.
- PostgreSQL
PostgreSQL, affectionately called Postgres, is the database giant that won't give you a "sql-ache". It's an open-source relational database that juggles SQL compliance with, throwing in enough advanced features that you'd think it’s doing data magic.
- Redis
Redis is like that flash memory card that surprises you with its speed every time. It's an in-memory data structure store, used as a database, cache, and message broker. It’s like giving your data a triple espresso shot, so your app's data-fetching game is always on point.
- AWS
AWS, or Amazon Web Services, is the colossal cloud playground where developers deploy their apps without ever worrying about running out of sandbox space. It's a haven of scalable resources, with enough services to make any developer feel like a kid in a candy store, or rather, a techie in a tech store.