Data Engineer with Tableau 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 Tableau 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 Tableau used?
Sales Sizzle
- Boosting biz with zingy sales dashboards, Tableau is like a sales team's culinary spice, turning bland data into flavor-burst charts that sell like hotcakes!
Marketing Magic Brew
- Serving up bewitching campaigns? Tableau concocts potion-like visualizations, making sense of the cauldron-bubble of market trends and customer preferences.
Operational Oracle
- In the labyrinth of logistics, Tableau lights the way, predicting fouls and turning operational chaos into a streamlined symphony.
Customer Chronicles
- It's like speed-dating with datasets - Tableau helps you woo and understand your customers, making their stories as captivating as a first date!
Tableau Alternatives
Power BI
Microsoft's Power BI is a business analytics service providing non-technical users with tools for aggregating, analyzing, visualizing, and sharing data.
// Comparison between Power BI and Tableau often involves language used
// DAX in Power BI:
CALCULATE(SUM(Sales[Amount]), Sales[Country] = "USA")
// Tableau's equivalent, using calculated fields:
SUM(IF [Country] = "USA" THEN [Amount] ELSE 0 END)
- More affordable than Tableau.
- Tightly integrated with other Microsoft products.
- Less robust on very large data sets.
Qlik Sense
Qlik Sense is a self-service data visualization and discovery application designed for individuals, groups, and organizations.
// Load script in Qlik Sense to transform data, unlike drag-and-drop interface in Tableau
LOAD Customer,
OrderDate,
Sales
FROM SalesData.qvd(qvd);
- Unique associative analytics engine.
- Flexible and scalable.
- May have a steeper learning curve.
Looker
Looker is a data exploration platform that helps companies get value from their data with real-time insights benefiting all departments.
// LookML code shows data modeling in Looker, differing from Tableau's approach
view: order_items {
measure: total_revenue {
type: sum
sql: ${sale_price} * ${quantity} ;;
}
}
- Highly customizable with LookML.
- Seamless integration with Google Cloud Platform.
- Less intuitive for casual users.
Quick Facts about Tableau
Birth of a Data Wizard: Tableau's Enchantment Begins
Picture it: 2003, a world drowning in data but gasping for insight. Along come Stanford's data viz magicians, Christian Chabot, Pat Hanrahan, and Chris Stolte, waving their wands of computer science wizardry to conjure up Tableau. Poof! Suddenly, data's not just numbers; it’s stories waiting to be told.
Disrupt-o-Matic 9000: Tableau Shakes Up Data Analysis
In the dull sea of spreadsheets, Tableau emerged as the rainbow unicorn, transforming how humans play with data. Its drag-and-drop sorcery made analytics as easy as swiping right. 2010 marked the debut of Tableau Public, the free platform for data-storytellers, making data analysis hotter than a summer romance.
Viva la Evolution: Tableau’s Ever-Spinning Upgrade Wheel
Life’s constant, change, and Tableau gets it. Like a chameleon on a disco floor, it’s always evolving. Cue Tableau 2020.2, dropping the mind-bending feature ‘Relationships’ – a breakthrough letting tables relate without getting hitched, simplifying data models like never before. It’s data relationship therapy at its finest!
What is the difference between Junior, Middle, Senior and Expert Tableau developer?
| Seniority Name | Years of Experience | Responsibilities & Activities | Average Salary (USD/Year) |
|---|---|---|---|
| Junior | 0-2 |
| 50,000-70,000 |
| Middle | 2-5 |
| 70,000-95,000 |
| Senior | 5-10 |
| 95,000-120,000 |
| Expert/Team Lead | 10+ |
| 120,000-150,000+ |
Top 10 Tableau Related Tech
SQL
Let's start with the cool granddaddy of data manipulation: SQL, or as I like to refer to it, the "Swiss Army knife" for data enthusiasts. SQL is to Tableau what garlic is to Italian food – it enhances everything! With its querying superpowers, SQL allows you to slice and dice data, making it a primo pick when prepping your numbers for a visual feast in Tableau.
SELECT dish, COUNT(*)
FROM kitchen
WHERE chef = 'Gordon Ramsay'
GROUP BY dish;
R or Python
Next in line, R and Python are kind of like the Batman and Superman of data analysis. They're both mighty on their own, but when you use them with Tableau, it's like the Justice League of data visualization! Use these languages for advanced analytics before unleashing the results onto Tableau's dashboard for some eye-candy charts.
# Python snippet for data munging
import pandas as pd
df = pd.read_csv('delicious_data.csv')
df['tasty_metric'] = df['sugar'] / df['spice']
Tableau Prep
Then we have Tableau Prep, a.k.a. the data wrangler. It's like the sous-chef that does all the prep work so that when you're ready to cook up some visualizations, everything is chopped, seasoned, and marinated to perfection!
// Hypothetical Tableau Prep code
Input: "raw_ingredients.csv"
Clean: UpperCase("ProductName")
Output: "prepped_data_for_viz.hyper"
Databases (MySQL, PostgreSQL, etc.)
Oh, databases – the trusted vaults for your precious data treasures. Whether it's MySQL's ease of use or PostgreSQL's robustness, you're gonna want to cozy up with these storage units since Tableau adores sifting through them to help create your masterpiece dashboards.
ETL Tools (Informatica, Talend, etc.)
ETL tools are the movers and shakers in the data world. Picture them like those fancy airport conveyor belts that get your luggage from check-in to your final destination. They efficiently transport and transform your data, ensuring it's ready for the glam lights of Tableau.
Apache Spark
If you're dealing with mountains of data, you're gonna want a sherpa like Apache Spark to help trek through it all. Spark is like having a jetpack that accelerates data processing, which means less time crunching numbers and more time charming folks with your dazzling Tableau visuals.
JavaScript API
Sometimes, you've got to get fancy with your Tableau dashboards, and that's where JavaScript API sashays in with a little extra sparkle. Want to integrate your Tableau visual into a web app? Bam! JavaScript API makes it happen, leaving your audience ooh-ing and aah-ing.
// JavaScript snippet to embed a Tableau viz
var containerDiv = document.getElementById("vizContainer"),
url = "http://tableau.server/views/SuperAwesomeViz";
var viz = new tableau.Viz(containerDiv, url);
Tableau Server
Tableau Server is like the stage for the grand performance of your data. It’s where your visualizations go live and are shared with the data-hungry audience. Understanding its ins and outs is crucial for ensuring your visualizations are always ready for their prime time.
APIs for Data Sources (Facebook, Twitter, Google Analytics)
APIs for various data sources are the behind-the-scenes ninjas that silently sneak in a heap of data into Tableau. While nobody sees them, their power is undeniable, fetching data from social media giants and digital analytics platforms straight into your projects!
Cloud Platforms (AWS, Azure, Google Cloud)
Last but not least, let's talk cloud platforms – the luxurious high-rise apartments of the data world. AWS, Azure, and Google Cloud offer a swanky residence for your data, ensuring high availability and scalability to serve up Tableau viz that's as reliable as a Swiss watch.