Data Engineer with Snowflake 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 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.
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 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 Name | Years of Experience | Average Salary (USD/year) | Responsibilities & Activities |
|---|---|---|---|
| Junior Snowflake Developer | 0-2 | 50,000 - 70,000 |
|
| Middle Snowflake Developer | 2-5 | 70,000 - 95,000 |
|
| Senior Snowflake Developer | 5-10 | 95,000 - 125,000 |
|
| Expert/Team Lead Snowflake Developer | 10+ | 125,000 - 150,000+ |
|
Top 10 Snowflake Related Tech
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';
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'
)
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.
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
$$
if (file_name.endsWith('.csv')) {
// Let's load the CSV, shall we?
}
return "File processed successfully.";
$$
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.
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.
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.
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.
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.
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.