Data Engineer with SQL 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 SQL 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 SQL used?
E-commerce Personalization
- SQL turns into a retail whisperer, nudging databases to reveal your shopping kryptonite to tailor those pesky ads.
The Matchmaker Databases of Dating Apps
- SQL plays cupid, sifting through zillions of profiles to find your potential 'swipe right' using complex JOINs for your happily ever after.
Gaming Industry’s Secret Sauce
- In the gaming realm, SQL is the loot box that spawns monster stats and keeps track of who's looting too much cheese.
Financial Forecasting Wizardry
- Abracadabra! SQL waves its wand to turn numbers into charts, helping bankers predict if it's going to rain money or financial frogs.
SQL Alternatives
MongoDB
MongoDB is a NoSQL database program using JSON-like documents with optional schemas. It suits large-scale data storage, real-time analytics, and rapid development.
// SQL:
SELECT * FROM users WHERE age > 25;
// MongoDB:
db.users.find({age: {$gt: 25}})
- Schema-less: Flexibility in data representation.
- Horizontal scalability: Can handle large data sets.
- Performance: Fast queries for unstructured data.
- Complex transactions: Less ACID-compliant than SQL.
- Join operations: Not as straightforward as SQL.
- Consistency: Eventual consistency can be an issue for some applications.
Redis
Redis is an in-memory data structure store used as a database, cache, and message broker supporting varied data structures.
// SQL:
UPDATE sessions SET data = 'new_data' WHERE session_id = 'XYZ';
// Redis:
SET session:XYZ "new_data"
- Performance: Extremely fast due to in-memory computation.
- Flexibility: Supports various data structures.
- Simple design: Easy to use for caching.
- Persistence: Less durable than disk-based databases.
- Memory usage: Can be costly for larger datasets.
- Data size: Limited to available memory.
Cassandra
Apache Cassandra is a distributed NoSQL database handling large amounts of data with no single point of failure, ensuring high availability.
// SQL:
SELECT * FROM users WHERE last_name = 'Smith';
// Cassandra:
SELECT * FROM users WHERE last_name = 'Smith' ALLOW FILTERING;
- Scalability: Efficiently scales out across multiple nodes.
- High availability: Designed for fault tolerance and replication.
- Write performance: Fast writes due to log-structured design.
- Consistency: Tunable, but can be complex to manage.
- Query support: Less flexible query language compared to SQL.
- Learning curve: Steeper due to unique architecture and design principles.
Quick Facts about SQL
SQL: A Relic of Database Chatter!
Way back in 1974, a group called the IBM San Jose Research Laboratory birthed what would become the chatty Cathy of database languages, SQL. Amidst the groovy era, Donald D. Chamberlin and Raymond F. Boyce waltzed in with what they dubbed SEQUEL, later renamed to avoid a scuffle with a trademark. Their brainchild has ever since been the go-to gabfest for database die-hards, letting folks yammer with tables and queries.
SQL's "Stand-Up" Act in '86!
In the neon glow of the 80s, specifically 1986, SQL got its big break when the American National Standards Institute (ANSI) gave it a standing O as a standard. Its encore? The International Organization for Standardization (ISO) followed suit in 1987. This was SQL’s version of going platinum, turning it from cool kid on the block to the lingua franca of database dialects worldwide.
The Ever-Sprouting SQL Sycamore!
SQL is like that one tree in your yard that keeps sprouting new branches. Since it burst onto the scene, it's had a makeover more times than a reality TV star. With each new rendition – from SQL-89 to SQL:2019 – it's packed in more tricks, like handling JSON data, recursive queries, and window functions. It's fair to say, SQL's growth spurt is far from over.
-- Ye olde SQL whispering spells to conjure up employee names:
SELECT first_name, last_name
FROM employees;
And so, dear database wizards, venture forth and sling SQL spells with the wisdom of its quirky past!
What is the difference between Junior, Middle, Senior and Expert SQL developer?
| Seniority Name | Years of Experience | Average Salary (USD/year) | Quality-wise | Responsibilities & Activities |
|---|---|---|---|---|
| Junior SQL Developer | 0-2 | 50,000-70,000 | Learning and mastering basics |
|
| Middle SQL Developer | 2-5 | 70,000-90,000 | Refining skills, taking on complex tasks |
|
| Senior SQL Developer | 5-10 | 90,000-120,000 | Expert problem-solving, mentoring |
|
| Expert/Team Lead SQL Developer | 10+ | 120,000+ | Strategic planning, leadership |
|
Top 10 SQL Related Tech
SQL Dialects: The Tower of Babel in Database Land
SQL dialects are like the various accented English spoken around the world – you think everyone understands you until you ask for "tomatoes" in a British grocery store. Get familiar with the quirks of each – be it T-SQL for Microsoft's SQL Server or PL/SQL for Oracle. They all promise to organize your data, but they'll each do it with their own flair.SQL Server Management Studio (SSMS): The Swiss Army Knife
SSMS is the go-to toolkit for anyone who needs to meddle with SQL Server databases. It's like walking into a data dungeon with a glowing sword of insights, allowing you to query, design, and manage your databases and data warehouses with the elegance of a knight at a medieval banquet.MySQL Workbench: Your SQL Soulmate
MySQL Workbench is the blind date that went surprisingly well. Originally unsure about its awkward interface, developers soon find it comforting, helpful, and powerful for designing, modeling, and managing MySQL databases. Plus, it's great for visual types who like to draw out their database relationships rather than spell them out.PostgreSQL: The Elephant in the Room
PostgreSQL, affectionately known as Postgres, is the wise old elephant that never forgets a datum. It's comprehensive, robust, and has more features than a Swiss knife. It's the go-to for developers looking for more than just a data storage system but a full-fledged relational experience with toys like JSON support and concurrency without read locks.SQLite: The Minimalist's Dream
SQLite is the pocket-sized, low-maintenance pet you never knew you needed. Lives directly in apps with zero configuration, it's like a simple notepad for your data – no frills, just writes and reads. And it’s so lightweight, you can almost forget it's there...until you need that crucial piece of data while offline in a remote forest.Microsoft Azure SQL Database: The Cloud Juggler
Microsoft Azure SQL Database is like hiring a cloud that follows you around all day, holding your data. It scales on-demand, backs up your life automatically, and promises a 99.99% up-time, which is more reliable than your average superhero.Oracle Database: The Ancient Reliquary
This is the granddaddy of databases, so mature and feature-rich that it's like a walking encyclopedia of data management knowledge. However, be ready to dedicate a significant chunk of your life to studying its ancient texts and offerings if you wish to unlock its full potential.ORMs (Object-Relational Mapping): The Translator
ORMs are like that friend who knows just a little too many languages. They translate your object-oriented languages such as Python, Ruby, or JavaScript into SQL so smoothly that you might forget that not everyone speaks "Database" natively. Some crowd favorites are Hibernate, Entity Framework, and Sequelize.// Example in JavaScript with Sequelize
const User = sequelize.define('user', {
username: Sequelize.STRING,
birthday: Sequelize.DATE
});
User.create({
username: 'techwizard',
birthday: new Date(1991, 0, 1)
});
NoSQL Databases: The Nonconformist's Choice
On the opposite side of the strict traditional SQL databases, NoSQL came along like a rebellious teenager refusing to fit into rows and columns. Think MongoDB or Cassandra, perfect for when your data is more "free-spirit" than "strict librarian" and doesn't like being boxed in by schemas.Data Visualization Tools: SQL's Crystal Ball
Once you've queried your heart out and have the data, tools like Tableau or Microsoft Power BI help you peer into the crystal ball to make sense of it all. These tools are the fortune tellers in the SQL world, turning numbers and strings into prophetic insights via charts, graphs, and dashboards.