Data Analyst (DA) with Tableau Developer Salary in 2024

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Median Salary Expectations:

How statistics are calculated

We count how many offers each candidate received and for what salary. For example, if a Data Analyst (DA) 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.

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,
FROM SalesData.qvd(qvd);

  • Unique associative analytics engine.


  • Flexible and scalable.


  • May have a steeper learning curve.



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 NameYears of ExperienceResponsibilities & ActivitiesAverage Salary (USD/Year)

  • Assist in the creation and maintenance of Tableau dashboards

  • Learn Tableau and data visualization best practices

  • Perform basic data analysis under supervision


  • Develop and optimize Tableau dashboards independently

  • Interpret data and provide insights to stakeholders

  • Contribute to data modeling and preparation processes


  • Lead complex data visualization projects

  • Mentor junior and middle Tableau developers

  • Ensure adherence to data governance and security policies

Expert/Team Lead10+

  • Define the strategic direction for data visualization and BI tools

  • Coordinate cross-functional teams for data-driven decision-making

  • Drive innovation and improve organizational BI capabilities



Top 10 Tableau Related Tech

  1. 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;

  1. 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']

  1. 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"

  1. 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.

  1. 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.

  1. 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.

  1. 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);

  1. 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.

  1. 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!

  1. 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.


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