Data Engineer with AWS (Amazon Web Services) Salary in 2024
How statistics are calculated
We count how many offers each candidate received and for what salary. For example, if a Data Engineer 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.
Trending Data Engineer tech & tools in 2024
Data Engineer
What is a data engineer?
A data engineer is a person who manages data before it can be used for analysis or operational purposes. Common roles include designing and developing systems for collecting, storing and analysing data.
Data engineers tend to focus on building data pipelines to aggregate data from systems of record. They are software engineers who put together data and combine, consolid aspire to data accessibility and optimisation of their organisation’s big data landscape.
The extent of data an engineer has to deal with depends also on the organisation he or she works for, especially its size. Larger companies usually have a much more sophisticated analytics architecture which also means that the amount of data an engineer has to maintain will be proportionally increased. There are sectors that are more data-intensive; healthcare, retail and financial services, for example.
Data engineers carry out their efforts in collaboration with particular data science teams to make data more transparent so that businesses can make better decisions about their operations. They use their skills to make the connections between all the individual records until the database life cycle is complete.
The data engineer role
Cleaning up and organising data sets is the task for so‑called data engineers, who perform one of three overarching roles:
Generalists. Data engineers with a generalist focus work on smaller teams and can do end-to-end collection, ingestion and transformation of data, while likely having more skills than the majority of data engineers (but less knowledge of systems architecture). A data scientist moving into a data engineering role would be a natural fit for the generalist focus.
For example, a generalist data engineer could work on a project to create a dashboard for a small regional food delivery business that shows the number of deliveries made per day over the past month as well as predictions for the next month’s delivery volume.
Pipeline-focused data engineer. This type of data engineer tends to work on a data analytics team with more complex data science projects moving across distributed systems. Such a role is more likely to exist in midsize to large companies.
A specialised, regionally based food deliveries company could embark upon a pipeline-oriented project, building an analyst tool that allows data scientists to comb through metadata to retrieve information about deliveries. She could look at distances travelled and time spent driving to make deliveries in the past month, and then input those results into a predictive algorithm that forecasts what those results mean about how they should do business in the future.
Database centric engineers. The data engineer who comes on-board a larger company is responsible for implementations, maintenance and populating analytics databases. This role only comes into existence where data is spread across many databases. So, these engineers work with pipelines, they might tune databases for particular analysis, and they come up with table schema using extract, transform and load (ETL) to copy data from several sourced into a single destination system.
In the case of a database-centric project at a large, national food delivery service, this would include designing an analytics database. Beyond the creation of the database, the developer would also write code to get that data from where it’s collected (in the main application database) into the analytics database.
Data engineer responsibilities
Data engineers are frequently found inside an existing analytics team working alongside data scientists. Data engineers provide data in usable formats to the scientists that run queries over the data sets or algorithms for predictive analytics, machine learning and data mining type of operations. Data engineers also provide aggregated data to business executives, analysts and other business end‑users for analysis and implementation of such results to further improve business activities.
Data engineers tend to work with both structured data and unstructured data. Structured data is information categorised into an organised storage repository, such as a structured database. Unstructured data, such as text, images, audio and video files, doesn’t really fit into traditional data models. Data engineers must understand the classes of data architecture and applications to work with both types of data. Besides the ability to manipulate basic data types, the data engineer’s toolkit should also include a range of big data technologies: the data analysis pipeline, the cluster, the open source data ingestion and processing frameworks, and so on.
While exact duties vary by organisation, here are some common associated job descriptions for data engineers:
- Build, test and maintain database pipeline architectures.
- Create methods for data validation.
- Acquire data.
- Clean data.
- Develop data set processes.
- Improve data reliability and quality.
- Develop algorithms to make data usable.
- Prepare data for prescriptive and predictive modeling.
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 Name | Years of Experience | Average Salary (USD/year) | Responsibilities & Activities |
|---|---|---|---|
| Junior | 0-2 years | $70,000 - $90,000 |
|
| Middle | 2-5 years | $90,000 - $120,000 |
|
| Senior | 5-10+ years | $120,000 - $150,000 |
|
| Expert/Team Lead | 10+ years | $150,000 - $180,000+ |
|
Top 10 Amazon Web Services (AWS) Related Tech
- 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! - 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. - 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. - 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. - 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! - 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. - 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. - 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. - 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. - 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.