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Data Engineer with Snowflake Salary in 2024

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Total:
91
Median Salary Expectations:
$5,050
Proposals:
0.6

How statistics are calculated

We count how many offers each candidate received and for what salary. For example, if a Data Engineer with Snowflake 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 Snowflake used?

 

 
 

 


Data Warehouse on Steroids



    • Imagine a digital library, except it's for data—and it never yells at you for late returns. That's Snowflake as a data warehouse, comically oversized storage for all the zeros and ones!



Analytics Gymnastics



    • Picture a gymnast flipping through data instead of the air—Snowflake's analytics tools twist and tumble through numbers like an Olympian, minus the spandex.



Scaling Everest, Digitally



    • With Snowflake, scaling up is as easy as a Yeti’s climb up Everest. It expands resources on-the-fly, handling big data without breaking a digital sweat.



Sharing is Caring



    • Think of Snowflake's data sharing as the kindergarten of cloud services, where everyone gets a piece of the data pie, but with better manners and no afternoon naps.


Snowflake Alternatives

 

Amazon Redshift

 

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It allows massive data storage and analysis.

 


-- Example for creating a table
CREATE TABLE users (
userid INTEGER NOT NULL PRIMARY KEY,
username CHAR(8),
first_name VARCHAR(30),
last_name VARCHAR(30)
);



    • Scalable to petabytes of data

 

    • Integration with AWS ecosystem

 

    • Columnar storage for fast queries

 

    • Upfront cost can be higher

 

    • Less dynamic scaling compared to Snowflake

 

    • Complex pricing model




Google BigQuery

 

Google BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse designed for business agility.

 


-- Example for running a query
SELECT name, COUNT(*) as num
FROM `bigquery-public-data.samples.shakespeare`
GROUP BY name;



    • Serverless and easy to set up

 

    • High query performance with large datasets

 

    • Seamless integration with Google Cloud services

 

    • Can be expensive for long-running operations

 

    • No native support for ETL (requires Dataflow)

 

    • Query performance can be inconsistent




Microsoft Azure Synapse Analytics

 

Azure Synapse is an integrated analytics service that accelerates time to insight across data warehouses and big data systems.

 


-- Example for loading data
COPY INTO SalesLT.Product
FROM 'https://myStorageAccount.blob.core.windows.net/myContainer/Products/'
WITH (
FILE_TYPE = 'CSV',
FIELDTERMINATOR = ',',
ROWTERMINATOR = '\n',
ENCODING = 'UTF8'
);



    • End-to-end analytics platform

 

    • Tightly integrated with other Azure services

 

    • On-demand or provisioned resources

 

    • Learning curve for new users

 

    • Can get expensive for high volumes

 

    • Limited to the Microsoft ecosystem

 

Quick Facts about Snowflake

 

Who Spawned the Snowflake?

 

Imagine a world where databases are sluggish behemoths, then *bam* Snowflake bursts onto the scene as the speedster of data warehousing. Created in 2012 by a trio of tech wizards—Benoit Dageville, Thierry Cruanes, and Marcin Zukowski—this platform said 'adieu' to hardware constraints and 'hello' to cloud elasticity. Now that's like swapping a tortoise for a unicorn!



The Cool Innovations of Snowflake

 

Groundbreaking is so last season. Snowflake brought in the avalanche with its data-sharing prowess, giving users the ability to share live data without breaking a sweat or copying a byte. And let's not overlook the multi-cluster, shared data architecture which is fancier than a double-decker pizza! It lets companies scale up and down faster than kids bailing on a broccoli dinner.



Debut of the Data Dynamo

 

Like the secret release of a blockbuster superhero movie, Snowflake went public in 2020 with one of the largest software IPOs ever, raking in a cool $3.4 billion. Watching their stock soar was like seeing a financial rocket take off, minus the countdown! And yes, it put Snowflake's name up in the data sky alongside the stars.




// Here's a cheeky sample of creating a Snowflake storage integration:
CREATE STORAGE INTEGRATION my_integrations
TYPE = EXTERNAL_STAGE
STORAGE_PROVIDER = S3
ENABLED = TRUE
STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::123456789012:role/my-role'
STORAGE_ALLOWED_LOCATIONS = ('s3://my-bucket/my-path/');

What is the difference between Junior, Middle, Senior and Expert Snowflake developer?



Seniority NameYears of ExperienceAverage Salary (USD/year)Responsibilities & Activities
Junior Snowflake Developer0-250,000 - 70,000

    • Assist with data migration to Snowflake

    • Perform simple database optimizations

    • Develop basic SQL queries for analysis

    • Maintain data pipeline under supervision


Middle Snowflake Developer2-570,000 - 95,000

    • Design data models and schemas

    • Handle moderate ETL processes

    • Implement security measures within Snowflake

    • Optimize existing Snowflake solutions


Senior Snowflake Developer5-1095,000 - 125,000

    • Architect and lead Snowflake-based projects

    • Design complex ETL pipelines

    • Develop advanced analytical SQL queries

    • Guide performance tuning of Snowflake systems


Expert/Team Lead Snowflake Developer10+125,000 - 150,000+

    • Steer product development with Snowflake

    • Lead cross-functional development teams

    • Define best practices and standards

    • Conduct high-level optimizations and troubleshooting


 

Top 10 Snowflake Related Tech



    1. SQL – The Snowy Spine of Data


      Imagine a snowball fight without snowballs, that's coding for Snowflake without SQL. As the lingua franca of data manipulation, a solid grip on SQL is paramount. From crafting queries to perform analytics to sculpting database structures, SQL is the gatekeeper to the winter wonderland of Snowflake data wonders.



      SELECT * FROM winter_castle WHERE elegance = 'majestic';

 

    1. Python – The Serpentine Sledge


      Python slithers through data like a sledge through fresh powder. Its simplicity and power make it ideal for data manipulation, Snowflake integration, and gliding through data lakes with libraries like Pandas and Snowflake Connector for Python.



      import snowflake.connector
      # Connect to the Snowflake slopes.
      conn = snowflake.connector.connect(
        user='ABOMINABLE_SNOWDEV',
        password='letItCode!',
        account='winter1234'
      )

 

    1. Snowflake Web UI – The Frosty Dashboard


      The Snowflake Web UI is your dashboard for a sleigh ride through your data. With point-and-click simplicity, any snowperson can manage warehouses, databases, and data sharing without scripting a single incantation.

 

    1. JavaScript – The Icy Glue


      Good ol' JavaScript, it sticks to Snowflake Stored Procedures like ice to a lamppost. Ideal for creating complex logic within Snowflake, JavaScript brings web development nimbleness to the frosty realm of in-database processing.



      CREATE OR REPLACE PROCEDURE file_loader(file_name STRING)
      RETURNS STRING
      LANGUAGE JAVASCRIPT
      AS
      $$
      if (file_name.endsWith('.csv')) {
        // Let's load the CSV, shall we?
      }
      return "File processed successfully.";
      $$

 

    1. Git – The Version Control Yeti


      Git: The cosmically cool yeti of version control, vital for collaborative projects in Snowflake development. It tracks changes, branches realities, and collaborates with other snow creatures using platforms like GitHub or GitLab.

 

    1. DBT (Data Build Tool) – The Snow Architect


      DBT is like handing a yeti a blueprint, automating the construction of complex data models in Snowflake. It takes raw data and transforms it with pure SQL magic, making it the perfect playmate for data analysts and engineers.

 

    1. Tableau or Power BI – The Northern Light Analysts


      Tableau and Power BI illuminate insights from Snowflake's snowy plains. These Business Intelligence tools conjure up vivid visualizations and dashboards, allowing data to sing and dance in the moonlight of decision-making.

 

    1. Docker – The Containerized Igloo


      Docker parcels up applications like snug little igloos, keeping them warm and self-contained for deployment on any icy server landscape. It's the perfect tool for ensuring your Snowflake integrations don't freeze up.

 

    1. Terraform – The Blizzard of Infrastructure


      Whipping up a storm, Terraform provisions and governs infrastructure with the force of a blizzard. For Snowflake, it lays down the frosty foundations, managing cloud resources so developers can focus on flinging snowballs rather than shoveling them.

 

    1. JIRA – The Agile Snowplow


      JIRA is the mighty snowplow of project management, ploughing through issues and features with an agility that makes yeti dancers jealous. In the Snowflake development tundra, it keeps user stories from getting buried under an avalanche of tasks.

 

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