Business Intelligence (BI) Developer with GCP (Google Cloud Platform) Salary in 2024

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Median Salary Expectations:

How statistics are calculated

We count how many offers each candidate received and for what salary. For example, if a Business Intelligence (BI) 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.

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`

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 NameYears of ExperienceAverage Salary (USD/year)Responsibilities & Activities
Junior GCP Developer0-2$70,000 - $100,000

  • Follow guidance to deploy basic GCP workloads

  • Managing smaller scale GCP components

  • Perform routine maintenance and debugging tasks

  • Contribute to internal knowledge bases

  • Participate in learning and development programs

Middle GCP Developer2-5$100,000 - $130,000

  • Develop scalable Google Cloud applications

  • Leverage GCP services to optimize resources

  • Support CI/CD pipelines for application deployments

  • Conduct basic system optimizations and monitoring

  • Assist in design and architecture discussions

Senior GCP Developer5-10$130,000 - $160,000

  • Design complex cloud solutions leveraging GCP

  • Lead cross-functional cloud projects

  • Perform advanced troubleshooting and provide mentorship

  • Optimize cloud costs and performance

  • Develop policies and best practices for cloud governance

Expert/Team Lead GCP Developer10+$160,000 - $200,000+

  • Steer cloud strategy and implementation across the organization

  • Make high-level design choices and dictate technical standards, tools, and platforms

  • Build and lead a team of GCP developers

  • Engage with stakeholders to understand business objectives

  • Drive innovation and adoption of cutting-edge cloud technologies


Top 10 Google Cloud Platform (GCP) Related Tech

  1. 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 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');



  1. 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"



  1. 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"



  1. 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 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!')



  1. 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



  1. 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 import pubsub_v1

    publisher = pubsub_v1.PublisherClient()
    topic_name = 'projects/my-project/topics/my-topic'
    publisher.publish(topic_name, b'My message!')



  1. 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



  1. 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
    - name: ''
    args: ['install']
    - name: ''
    args: ['test']



  1. 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"



  1. 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



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