Business Intelligence (BI) Developer with GCP (Google Cloud Platform) 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 GCP (Google Cloud Platform) 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 Google Cloud Platform (GCP) used?
Cloudy with a Chance of Big Data
- When data mountains feel like Everest, GCP hauls up the analytics backpack, puffs up BigQuery, and sleds down insights like a data pro.
Serverless Shenanigans
- GCP waves a magic wand, poof! Server management vanishes, Function clouds appear, devs throw confetti, and applications dance server-free!
Machine Learning Magic Show
- Like pulling AI rabbits out of hats, GCP's machine learning tools enable apps to predict, translate, and even see - no magic wands needed!
Kubernetes Keg Stand
- In the container party, GCP's Kubernetes juggles deployments like a frat star, scaling the fun without spilling a drop of efficiency.
Google Cloud Platform (GCP) Alternatives
Amazon Web Services (AWS)
Amazon Web Services is a comprehensive cloud platform offering over 200 fully-featured services from data centers globally. Services range from infrastructure technologies like compute, storage, and databases to machine learning, data analytics, and Internet of Things.
# Example of launching an EC2 instance with AWS SDK for Python (Boto3)
import boto3
ec2 = boto3.resource('ec2')
ec2.create_instances(ImageId='ami-0abcdef1234567890', MinCount=1, MaxCount=1, InstanceType='t2.micro')
- Extensive service offerings, with a wide range of tools.
- Diverse global infrastructure for high availability and fault tolerance.
- Complex pricing model with potential for high costs.
- May be overwhelming due to its vast amount of services and features.
- Strong track record in enterprise and government sectors.
Microsoft Azure
Microsoft Azure is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers. Includes PaaS and IaaS services and supports many different programming languages, tools, and frameworks.
# Example of deploying an Azure web app with Azure CLI
az webapp up --name MyUniqueAppName --resource-group MyResourceGroup --runtime "PYTHON:3.7"
- Integration with Microsoft tools and software.
- Hybrid cloud capabilities with Azure Stack.
- User interface is less intuitive compared to competitors.
- Can have higher learning curve for developers not familiar with Microsoft ecosystem.
- Growing suite of AI and machine learning services.
IBM Cloud
IBM Cloud includes a range of computing services from virtual servers to Watson AI. IBM Cloud is known for its focus on enterprise and cognitive solutions as well as hybrid multicloud and secure data governance.
# Example of creating a virtual server instance on IBM Cloud
ibmcloud is instance-create MyInstance us-south VPC-UniqueId subnet-0677-6789bdb83de9 --image image-7eb4b618-2ec3-4eed-937f-ff44fe18f9d7 --profile bx2-2x8
- Strong focus on AI and machine learning with Watson.
- Commitment to open-source with support for technologies like Kubernetes and Red Hat.
- UI and documentation can be less user-friendly than competitors.
- Smaller market share can mean fewer community resources.
- Advanced data security and encryption features.
Quick Facts about Google Cloud Platform (GCP)
The Dawn of Google's Cloud Odyssey
Cast your mind back to the halcyon days of 2008, a time when your phone was probably dumber than your fridge. In this year, the tech titans over at Google decided to bless the digital realm with the Google App Engine, the primordial ancestor of what we now bow to as Google Cloud Platform. This was Google doffing its cap to the cloud-computing craze, and boy, did they enter the fray with guns blazing!
Beast Mode: Google's Big Data and Machine Learning Muscle
It's no secret that Google loves data more than a pigeon loves a loaf of bread. Around 2014, they flexed their prodigious machine learning and big data muscles, introducing tools like BigQuery and Cloud Machine Learning Engine. This wasn't just a game-changer; it was a game-over for many a data-processing quandary. I mean, crunching data at the speed of thought? That's the digital equivalent of a mic drop.
# Here's a peep at how a simple BigQuery SQL looks like. Easy peasy!
SELECT name, COUNT(*) as num
FROM `bigquery-public-data.usa_names.usa_1910_current`
GROUP BY name
ORDER BY num DESC
LIMIT 10
Cloud Functions: A Serverless Utopia
Then came the year 2016, when the wizards of Google Cloud conjured up Cloud Functions. Oh, what sorcery! A world where you could run code without the hassle of servers! This was akin to throwing a feast and not doing dishes. The coder community rejoiced, for they could cast their incantations in Node.js, Python, Go, and more - all while Google's goblins managed the underlying infra-spell-work.
// A snippet of Node.js glory for a simple HTTP-triggered Cloud Function
exports.helloWorld = (req, res) => {
res.send('Hello, magical world of Serverless!');
};
What is the difference between Junior, Middle, Senior and Expert Google Cloud Platform (GCP) developer?
| Seniority Name | Years of Experience | Average Salary (USD/year) | Responsibilities & Activities |
|---|---|---|---|
| Junior GCP Developer | 0-2 | $70,000 - $100,000 |
|
| Middle GCP Developer | 2-5 | $100,000 - $130,000 |
|
| Senior GCP Developer | 5-10 | $130,000 - $160,000 |
|
| Expert/Team Lead GCP Developer | 10+ | $160,000 - $200,000+ |
|
Top 10 Google Cloud Platform (GCP) Related Tech
Python & Node.js – The Dynamic Duo
In the realm of GCP, Python slithers its way to the top with its ease of scripting and automation, while Node.js tags along with its non-blocking, event-driven architecture, making them an unstoppable tag-team for cloud-based applications. Both are like the peanut butter and jelly of cloud computing—universally loved and incredibly versatile.
# Python snippet connecting to GCP services
from google.cloud import storage
# Instantiates a client
storage_client = storage.Client()
# Node.js snippet for an HTTP Cloud Function
const http = require('http');
exports.helloWorld = (req, res) => {
res.writeHead(200, {'Content-Type': 'text/plain'});
res.end('Hello World\n');
};
Google Kubernetes Engine (GKE) – The Container Wrangler
Think of GKE as the shepherd of containerized flocks, guiding them effortlessly through the pastures of your cloud infrastructure. It’s the robust system that herds your Docker containers into manageable, scalable pods while ensuring they don't wander off the beaten path.
# Command to set up a GKE cluster
gcloud container clusters create "my-cluster"
Google Compute Engine (GCE) – The Brutish Workhorse
When it comes to raw computing power, GCE flexes its muscles with customizable virtual machines. It's like hiring a bodybuilder to do your heavy lifting, only this one can scale from the size of an ant up to the Hulk, depending on how much you feed it with your tasks.
# Command to create a VM instance
gcloud compute instances create "my-instance"
Google Cloud Storage – The Bottomless Toy Chest
Like a magical toy chest from a children's book, Google Cloud Storage can store an endless amount of data with no complaints. Object storage became just a little bit more awesome here, with near-infinite space for everything from backups to serving up website content.
# Python code to upload a blob to Google Cloud Storage
from google.cloud import storage
# Initialize a storage client
storage_client = storage.Client()
# Upload a blob
bucket = storage_client.get_bucket('my-bucket')
blob = bucket.blob('my-test-file')
blob.upload_from_string('This is test content!')
Google Cloud Functions – The Micro-Magic Performers
These are the tiny magicians of the serverless world, performing their single tricks reliably and without any need for a curtain call. They’re the specialists you call in when you want something done fast, simple, and without any of the heavy infrastructure tricks.
# Deploy a simple HTTP function
gcloud functions deploy helloGET --runtime nodejs10 --trigger-http --allow-unauthenticated
Google Cloud Pub/Sub – The Town Crier
Imagine a relentless orator in a bustling town square, delivering messages to anyone who’ll listen. Google Cloud Pub/Sub facilitates this seamless message exchange between services, anchoring asynchronous communication with its might.
# Python snippet for publishing a message to Pub/Sub
from google.cloud import pubsub_v1
publisher = pubsub_v1.PublisherClient()
topic_name = 'projects/my-project/topics/my-topic'
publisher.publish(topic_name, b'My message!')
Google Cloud BigQuery – The Data Detective
As the Sherlock Holmes of massive datasets, BigQuery sleuths through seas of information with its analytical magnifying glass, extracting insights at lightning speeds. It’s the tool you need when you have data puzzles begging to be solved.
# SQL query executed in BigQuery
SELECT name, age FROM 'project.dataset.table'
WHERE age > 30
Google Cloud Build – The Master Builder
Just like playing with LEGO bricks, Cloud Build assembles your code into neat deployable packages. It automates the steps from code committing to build, test, and deploy, ensuring that your software construction set doesn’t ever miss a brick.
# Build configuration in YAML for Cloud Build
steps:
- name: 'gcr.io/cloud-builders/npm'
args: ['install']
- name: 'gcr.io/cloud-builders/npm'
args: ['test']
Terraform – The Blueprint Boss
Terraform waves its wand and provisions infrastructure like it’s casting a spell. As the grand architect, it turns your GCP infrastructure designs into reality, treating your resources as code that can be versioned and tamed.
# Terraform snippet to create a simple GCE instance
resource "google_compute_instance" "default" {
name = "test-instance"
machine_type = "n1-standard-1"
zone = "us-central1-a"
}
Google Cloud SDK – The Swiss Army Knife
This indispensable tool is decked out with handy instruments to tweak and twiddle your GCP setup to your heart's content. Whether you're a plumber or a painter in the cloud, the Google Cloud SDK ensures you're never at a loss for the right tool.
# Command to authenticate with GCP
gcloud auth login