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Business Intelligence (BI) Developer with AWS (Amazon Web Services) Salary in 2024

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Total:
49
Median Salary Expectations:
$4,600
Proposals:
1

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 AWS (Amazon Web Services) 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.

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 Amazon Web Services (AWS) used?





Netflix: Binge-streaming with a Side of Cloud



  • AWS keeps Netflix chill by handling colossal traffic spikes every time a new season of "Stranger Things" drops.



NASA: To Infinity and Beyond, with Data!



  • When NASA needs to crunch universe-sized data sets or stream Mars rover selfies, AWS is their cosmic sidekick.



Formula 1: High-Octane Cloud Analytics



  • Revving up race strategies, AWS turbocharges F1 by analyzing streams of telemetry data faster than a pit stop.



Airbnb: Homey Feels with Cloud Deals



  • From cozy lofts to castle stays, Airbnb's endless list of getaways lives harmoniously in AWS's digital hammock.


Amazon Web Services (AWS) Alternatives

Google Cloud Platform (GCP)

A suite of cloud services offered by Google that runs on the same infrastructure that Google uses. Examples include Compute Engine and Google Kubernetes Engine.

 


# Example of creating a VM instance in GCP
gcloud compute instances create "my-vm" --zone "us-central1-a"



  • Integrated with Google's services
  • Strong data analytics tools
  • Frequent discounts and free credits
  • Less market share than AWS
  • User interface can be less intuitive
  • Complex pricing structure




Microsoft Azure

A set of cloud services for building, testing, deploying, and managing applications and services through Microsoft-managed data centers. Includes Azure Virtual Machines and Azure SQL Database.

 


# Example of deploying an app in Azure
az webapp up --location "CentralUS" --name "myAppName"



  • Seamless integration with Microsoft products
  • Extensive support for hybrid cloud
  • Strong emphasis on AI and machine learning
  • Higher learning curve for non-Microsoft devs
  • Can get expensive for larger operations
  • Less open-source friendly




IBM Cloud

IBM's cloud platform that provides a range of compute options, databases, and AI tools. Includes IBM Watson for AI and machine learning tasks.

 


# Example of starting a service in IBM Cloud
ibmcloud resource service-instance-create "my-instance" cloudantNoSQLDB Lite us-south



  • Strong enterprise focus
  • Powerful AI tools with Watson
  • Cognitive and data services
  • Complex interface and setup
  • Customer service can be slow
  • Potentially steep learning curve

 

Quick Facts about Amazon Web Services (AWS)

Once Upon a (Virtual) Server: AWS's Humble Beginnings

Believe it or not, the cloud empire of AWS sprouted from the digital soil in 2006, concocted by the e-commerce giant Amazon. They started with a simple yet audacious idea: "What if we rent out our massive computing infrastructure?" And just like that, AWS was birthed, offering their first service of on-demand computing. It was like giving a playground to tech nerds but making them pay for every swing and slide!

 

The API Symphony: AWS's Pioneering Moves

AWS was like the Pied Piper, leading the charge with their APIs; they released a basketful of them in 2006, letting developers manipulate virtual gadgets across the internet. Going beyond mere hosting, AWS brought the power of the force... I mean, "the cloud," to the average Joe Developer, allowing them to spawn servers with a few clicks or some magical incantations in their command lines.

 


# Summon an EC2 server like a digital wizard!
aws ec2 run-instances --image-id ami-0abcdef1234567890 --count 1 --instance-type t2.micro



S3: Storing the Internet Bit by Overflowing Bit

 

In the same year, 2006, AWS dropped S3, a.k.a. Simple Storage Service, but let's be honest, there's nothing simple about storing exabytes of data from all over the world. S3 made saving data as easy as stuffing a turkey on Thanksgiving. It allowed anyone to shove data into a virtual bucket and retrieve it faster than you can say "scalable storage solution." Suddenly, we had a hoarding solution for our digital trinkets!

 


# Hoard your bytes in an S3 bucket with a snap of your fingers!
aws s3 cp mySuperSecretFile.txt s3://mySuperCoolBucket/

What is the difference between Junior, Middle, Senior and Expert Amazon Web Services (AWS) developer?

 

Seniority NameYears of ExperienceAverage Salary (USD/year)Responsibilities & Activities
Junior0-2 years$70,000 - $90,000

  • Execute basic AWS tasks under supervision

  • Manage single-service deployments

  • Fix simple bugs identified by senior developers

  • Write code for small functionality

  • Learn best practices in AWS services


Middle2-5 years$90,000 - $120,000

  • Handle multi-service AWS deployments

  • Optimize existing AWS resources for cost and performance

  • Proactively identify potential issues and solve them independently

  • Contribute to the design of system architecture

  • Mentor junior developers


Senior5-10+ years$120,000 - $150,000

  • Architect complex multi-tier AWS solutions

  • Create and enforce AWS best practices across teams

  • Lead critical system upgrades or migrations

  • Conduct code reviews and ensure quality standards

  • Translate business needs into technical requirements


Expert/Team Lead10+ years$150,000 - $180,000+

  • Steer strategic direction for AWS adoption and optimization

  • Oversee multiple projects and ensure alignment with business goals

  • Lead and mentor teams of AWS developers

  • Drive innovation and stay abreast with the latest AWS technologies

  • Make key decisions on tech stack and tools


 

Top 10 Amazon Web Services (AWS) Related Tech

  1. Python & Boto3
    Here's Python donning a superhero cape, poised to rescue your cloudy day! With Boto3, Python's loyal sidekick, you can automate your AWS cloud faster than you can say "infrastructure-as-code." Need to launch an EC2 instance or play around with S3 buckets? Just a few lines of Python, and you're an AWS wizard!
  2. Serverless Framework
    Imagine deploying without servers; it's like magic without a wand. The Serverless Framework conjures AWS resources out of thin air. Just whisper your desired state, and presto! Your Lambda functions and API Gateways leap into the ether, ready to serve HTTP requests as eagerly as bellboys at a five-star hotel.
  3. Terraform
    No trick is too complex for the great Terraformini! Watch in amazement as he pulls whole networks out of his hat with Infrastructure as Code! Open-source and multi-cloud, he's the star of every cloud infrastructure show, making manual setup as outdated as a mime at a talking contest.
  4. AWS CLI
    If you prefer your commands served on a silver command-line interface, AWS CLI is your loyal butler. It brings the power of AWS to the comfort of your terminal, allowing you to whisper sweet nothings to manage your cloud resources. A simple "aws s3 ls" and your S3 buckets line up, ready for inspection.
  5. Docker
    Docker is the grandmaster of containerization, packaging your applications in containers so lightweight, they could float in the cloud. And with AWS's ECS or EKS, you can let those containers frolic across the cloud, scaling and rolling with the punches like acrobats in the software circus!
  6. JavaScript/Node.js
    To all the hipster devs sipping cold-brew: JavaScript, with its trusty Node.js steed, is ready to JS-ify your AWS ventures. Asynchronous, event-driven, and as trendy as your artisanal toast, Node.js lets your server-side applications shine brighter than your Instagram feed.
  7. Git & AWS CodeCommit
    Picture Git as the librarian of the coding world, carefully managing your source code with the meticulousness of a cat grooming itself. Pair it with AWS CodeCommit, and you’ve got a source control duo more dynamic than Batman and Robin, keeping your code safer than a squirrel’s nut stash in winter.
  8. Amazon RDS & DynamoDB
    Are you a digital gourmet who enjoys fine database dining? Amazon RDS sets the table with several flavors, be it PostgreSQL à la open-source or MySQL with a side of salsa. For those craving no-sql at its finest, DynamoDB is like an all-you-can-eat buffet, scaling to your appetite's content.
  9. Amazon CloudFormation
    Do you dream of constructing cloud infrastructure while you sleep? CloudFormation is like having your own legion of construction drones, building as per the blueprints of your JSON or YAML templates. Infrastructure becomes as pliable as playdough, assuming the shape of whatever fantastical form you command.
  10. AWS Lambda
    Fancy a world where servers are as passé as flip phones? AWS Lambda is your genie, ready to grant you serverless wishes. Just rub the Lambda lamp by deploying your code, and watch it execute with the grace of a ballet dancer whenever it's summoned by an event, free from the shackles of server management.
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