If you’re planning a career in Data Engineering, you’ve probably noticed two technologies appearing repeatedly in job descriptions: Snowflake and Databricks.
Both platforms are among the fastest-growing tools in modern data engineering, and companies across fintech, iGaming, banking, SaaS and AI are actively hiring professionals with experience in one—or both.
So which platform should you learn?
The answer depends on the type of work you want to do.
What Is Snowflake?
Snowflake is a cloud-based data warehouse designed to store, organise and analyse structured data.
It allows organisations to collect data from multiple sources and make it available for reporting, dashboards and business intelligence.
Companies use Snowflake because it is:
- Easy to scale
- Fast to query
- Fully managed
- Cloud-native
- Highly secure
Snowflake is particularly popular among organisations that rely heavily on reporting and analytics.
What Is Databricks?
Databricks is a unified data analytics platform built around Apache Spark.
Unlike Snowflake, Databricks is designed for much more than data warehousing.
It supports:
- Data Engineering
- Machine Learning
- Artificial Intelligence
- Data Science
- Real-time data processing
- Large-scale analytics
Databricks is ideal for organisations working with very large or complex datasets.
Snowflake vs Databricks: Key Differences
| Feature | Snowflake | Databricks |
|---|---|---|
| Primary Purpose | Cloud Data Warehouse | Data Engineering & AI Platform |
| Best For | Analytics & Reporting | Big Data & Machine Learning |
| SQL Support | Excellent | Excellent |
| Python Support | Good | Excellent |
| Apache Spark | Limited | Native |
| Machine Learning | Basic | Excellent |
| Business Intelligence | Excellent | Very Good |
| Data Science | Good | Excellent |
Although both platforms manage data, they solve different problems.
Which Platform Is Easier to Learn?
For beginners, Snowflake is generally easier.
If you’re already comfortable with SQL, you’ll find Snowflake relatively straightforward.
Databricks requires a broader understanding of:
- Python
- Spark
- Distributed computing
- Data engineering concepts
While Databricks has a steeper learning curve, it also offers more flexibility.
Which Skills Do Employers Want?
Modern Data Engineering jobs rarely focus on a single technology.
Employers commonly look for combinations such as:
Snowflake + SQL
Snowflake + Azure
Databricks + Spark
Databricks + Python
Airflow + Snowflake
Azure Data Factory + Databricks
Kafka + Databricks
Terraform + Snowflake
The more complementary skills you have, the more opportunities become available.
Industries Using Snowflake
Snowflake is widely used by:
- FinTech
- Banking
- Insurance
- Retail
- SaaS
- Business Intelligence teams
Companies that generate large amounts of business reporting often choose Snowflake because of its simplicity and performance.
Industries Using Databricks
Databricks is commonly used by organisations working with:
- Artificial Intelligence
- Machine Learning
- Big Data
- Financial Services
- Telecommunications
- Cloud-native platforms
Businesses processing massive datasets often choose Databricks because of its scalability.
Salary Comparison
Both platforms can lead to excellent salaries.
Typical annual gross salaries in Malta include:
| Experience | Typical Salary |
|---|---|
| Junior Data Engineer | €35,000 – €45,000 |
| Mid-Level Data Engineer | €50,000 – €65,000 |
| Senior Data Engineer | €65,000 – €80,000+ |
Salary depends far more on your overall experience than on whether you specialise in Snowflake or Databricks.
Engineers who combine cloud experience with either platform are often among the highest-paid professionals.
Should You Learn SQL or Python?
The best Data Engineers understand both.
Snowflake relies heavily on SQL.
Databricks relies heavily on Python and Spark.
Ideally, learn:
- SQL
- Python
- Cloud platforms
- Git
- ETL pipelines
These skills complement either platform.
Snowflake Strengths
Snowflake excels at:
- Business Intelligence
- Data Warehousing
- Analytics
- Fast SQL queries
- Secure data sharing
- Cloud scalability
- Ease of administration
It’s an excellent choice for organisations focused on reporting and analytics.
Databricks Strengths
Databricks is particularly strong for:
- Machine Learning
- AI workloads
- Spark processing
- Streaming data
- Large-scale ETL
- Data Lakehouse architecture
- Advanced analytics
It’s often the preferred platform for organisations building data-driven products.
Can You Learn Both?
Absolutely.
In fact, many organisations use both platforms together.
A common architecture might involve:
- Databricks for processing large datasets.
- Snowflake for storing curated business data.
- Power BI or Tableau for reporting.
Understanding how the platforms complement each other makes you a stronger Data Engineer.
Which One Should You Learn First?
If you’re new to Data Engineering:
Start with:
- SQL
- Snowflake
- Cloud fundamentals
Then move on to:
- Python
- Spark
- Databricks
- Airflow
This progression is often easier than starting directly with distributed computing.
Career Opportunities
Knowledge of either platform can lead to roles such as:
- Data Engineer
- Analytics Engineer
- Cloud Data Engineer
- Business Intelligence Engineer
- Data Platform Engineer
- Machine Learning Engineer
Demand for these roles continues to grow across Malta and Europe.
Final Thoughts
Choosing between Snowflake and Databricks isn’t really about deciding which platform is better.
They’re designed for different purposes and increasingly work together within modern cloud architectures.
If your goal is reporting, business intelligence and data warehousing, Snowflake is an excellent choice.
If you’re interested in big data, machine learning and advanced analytics, Databricks offers incredible opportunities.
For the strongest career prospects, aim to understand both platforms alongside SQL, Python and cloud technologies.
If you’re looking for your next Data Engineering opportunity, browse the latest Data Engineering jobs on SoftwareVacancy and discover employers hiring across Malta.
Frequently Asked Questions
Is Snowflake better than Databricks?
Neither platform is universally better. Snowflake focuses on cloud data warehousing and analytics, while Databricks specialises in big data processing, machine learning and advanced analytics.
Should beginners learn Snowflake or Databricks first?
Most beginners find Snowflake easier because it relies heavily on SQL. Databricks introduces additional concepts such as Apache Spark and distributed computing.
Do employers use both platforms?
Yes. Many organisations combine Databricks for data processing with Snowflake for analytics and reporting.
Which platform pays more?
Salary depends more on your overall experience and cloud expertise than on the platform itself. Engineers with Snowflake or Databricks skills are both highly sought after.
Where can I find Data Engineering jobs requiring Snowflake or Databricks?
SoftwareVacancy regularly publishes Data Engineering, Analytics Engineering and Cloud Data roles requiring Snowflake, Databricks, SQL and modern cloud technologies from employers across Malta.