Data QA Developer 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 QA 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 QA tech & tools in 2024
Data QA
What is Data Quality
A data quality analyst maintains an organisation’s data so that they can have confidence in the accuracy, completeness, consistency, trustworthiness, and availability of their data. DQA teams are in charge of conducting audits, defining the data quality standards, spotting outliers, and fixing the flaws, and play a key role at all stages in the data lifecycle. Without DQA work, strategic plans will fail, operations will go awry, customers will leave, and organisations will face substantial financial losses, as well as a lack of customer trust and potential legal repercussions due to poor-quality data.
This is a job that has changed as much as the hidden infrastructure that transforms data into insight and then powers the apps that we all use. I mean, it’s changed a lot.
Data Correctness/Validation
This is the largest stream of all the tasks. When we talk about data correctness, we should be asking: what does correctness mean to you, for this dataset? Because it would be different for every dataset and every organisation. The commonsense interpretation is that it must be what your end user (or business) wants from the dataset. Or what would be an expected result of the dataset.
We can obtain this just by asking questions, or else reading through the list of requirements. Here are some of the tests we might run, in this stream:
Finding Duplicates — nobody wants this in their data.
– Your data contains unique/distinct values in that column/field. Will the returned value be a unique/distinct value in that column/field?
– Any value that can be found in your data is returned.
Data with KPIs – If data has any columns we can sum, min or max on it’s called a key performance indicator. So basically any models which are mostly numeric/int column. eg: Budget, Revenue, Sales etc. If there is data comparison between two datasets then below tests applies:
– Comparing counts between two datasets — get the difference in count
– Compare the unique/distinct values and counts for columns – find out which values are not present in either of the datasets.
– Compare the KPIs between two datasets and get the percentage difference between them.
– Replace missing values – missing in any one of the datasets with primary or composite primary key. This can be done in a data source that does not have primary key too.
– Perform the metrics by segment for the individual column value — that can help you determine what might be going wrong if the count of values in the Zoopla-side doesn’t match the count on the Rightmove-side or if some of the values are missing.
Data Freshness
This is an easy set. How do we know if the data is fresh?
An obvious indication here is to check if your dataset has a date column, in which case, you just check the max date. Another one is, when the data was pulled into a particular table, all of this can be converted into a very simple automated checks, which we might talk about in a later blog entry.
Data Completeness
This could be an intermediate step in addition to data correctness, but how do we know to get there if the space of answers is complete?
To do this test, check if any column has all values null in it perhaps that’s okay, but most of the time it’s bad news.
Another test would be one-valuedness: whether everywhere on the column all values are the same, probably in some cases that would be a fine result, but probably in other cases that would be something we’d rather look into.
What are Data Quality Tools and How are They Used?
Data quality tools are used to improve, or sometimes automate, many processes required to ensure that data stays fit for analytics, data science, and machine learning. For example, such tools enable teams to evaluate their existing data pipelines, identify bottlenecks in quality, and even automate many remediation steps. Examples of activities relating to guaranteeing data quality include data profiling, data lineage, and data cleansing. Data cleansing, data profiling, measurement, and visualization tools can be used by teams to ‘understand the shape and values of the data assets that have been acquired – and how they are being collected’. These tools will call outliers and mixed formats. In the data analytics pipeline, data profiling acts as a quality control gate. And each of these are data management chores.
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.