Avaxia Snowflake Team’s Work Overview
Avaxia Snowflake Team creates and modifies data pipelines based on customer requirements.
In the project, we are building data pipelines that process Excel sheets uploaded from various locations, cleanses the data, and performs calculations to make it accessible to different departments as needed. Additionally, I am participating as a backend engineer in the development of a web application that allows data approval and simulations using this processed data. The API for this web application is developed in Python, and since Snowflake allows writing USP in Python, I feel that using the same language has significantly reduced the barriers between different team members.
Furthermore, we adopt an Agile development method for this project, which I find to be highly compatible with data pipeline development. Since customers do not have a complete understanding of all processes, and we are also continuously refining our implementations, we collaborate closely to build an efficient and effective pipeline.
Expertise in Data Engineering and Data Science Solutions on the Snowflake Platform
Our consultants at Avaxia possess deep expertise in delivering data engineering and data science solutions on the Snowflake platform. With a comprehensive understanding of Snowflake’s advanced features, our team excels in designing and implementing scalable, high-performance data pipelines, optimizing data storage, and ensuring seamless integration across cloud environments. We leverage Snowflake’s powerful capabilities to handle diverse data sources, performing complex transformations and analytics to generate actionable insights. Our consultants specialize in developing custom solutions for a wide range of business needs, from data migration and performance optimization to implementing machine learning models and advanced analytics on Snowflake. With a focus on delivering value through automation, real-time data processing, and cutting-edge data science techniques, our consultants are dedicated to driving impactful results for clients across various industries.
Difference Between Data Scientists (Analysts) and Data Engineers
Both data scientists (analysts) and data engineers work with data, but their roles and required skills differ significantly:
- Data Scientists (Analyst)：Focuses on analyzing and utilizing data to extract value.
- Data Engineers：Focuses on preparing, organizing, and managing data.
Let’s look at their differences.
1. What is Data Scientist (Analyst)？
A data scientist is responsible for deriving business insights from data and supporting decision-making.Their main tasks include:
Key Responsibilities
- Data analysis and modeling:Statistical analysis, machine learning model development
- Reporting and visualization using BI tools: Tableau, Power BI, Looker
- Hypothesis testing using A/B tests and casual inference
- Proposing data-driven solutions to business matters
Required Skills
- Programming: Python, R, SQLfor data analysis and machine learning
- Statistics and Machine Learning: Regression analysis, classification, clustering
- Data Visualization: BI tools, Matplotlib, Seaborn
- Business Understanding and Communication: Ability to convey the value of data effectively
2. What is Data Engineer?
A data engineer is responsible for collecting, transforming, and storing data to create an environment where it can be easily analyzed.Their main tasks include:
Key Responsibilities
- Developing and managing data pipelines: designing and implementing ETL/ELT processes
- Building and optimizing data warehouses: Snowflake, BigQuery, Redshift
- Improving data infrastructure performance: query optimization, index management
- Managing data and infrastructure in cloud environments: AWS, GCP, Azure
Required Skills
- Programing: SQL, Python, Spark for data processing and pipeline development
- Database and DWH Management: Designing and operating RDBMS, Snowflake, BigQuery
- ETL/ELT Tools: Knowledge of Airflow, dbt, Fivetran
- Cloud and Container Technologies: AWS, GCP, Dooker, Kubernetes
3. Difference Between Data Scientists and Data Engineers
| Category |
Data Scientist (Analyst) |
Data Engineer |
| Objective |
Analyze data to generate business value |
Manage data properly for analysis |
| Main Tasks |
Data analysis, prediction, visualization, machine learning |
Data infrastructure development, ETL optimization |
| Tools Used |
Python, R, SQL, Tableau, Power BI |
SQL, Python, Spark, Airflow, dbt |
| Required Skills |
Statistics, machine learning, data visualization |
Database design, ETL, cloud technologies |
| Business Involvement |
Supports decision-making through data utilization |
Prepares infrastructure to enable data utilization |
4. Collaboration Between Data Scientists and Data Engineers is Essential!
For data scientists to leverage data to support business decision-making, it is crucial that the data is properly collected and organized by the engineers.
For example:
- Data scientists analyze data using pipelines built by data engineers.
- Data scientists communicate new analytical needs to data engineers to expand the data infrastructure.
By working together, both roles enable more advanced and effective data utilization.
5. Which Career Path Should You Chose?
- If you are interested in data analysis and utilization → Data Scientist (Analyst)
- If you are interested in building data infrastructure and system development → Data Engineer
Both roles are essential for effective data utilization and are in high demand. With the growing adoption of cloud data warehouses like Snowflake, the role of data engineers is becoming important, while data scientists are expected to perform even more advanced analyses.
I originally started as a developer and later transitioned into a consultant. I recently revisited my studies in data science. When I think about data engineering, I realize that during my time as a developer, I was already extracting data from enterprise servers through batch processing, transforming it to make it easily accessible for various departments, and developing applications to display that data. In that sense, the work I am doing now is not all that different. As for data science, it is a field I enjoy, having taught it as a part-time instructor at a technical school.
At Avaxia, we provide optimal solutions to support advanced data utilization using Snowflake. If you are interested or would like further information, please contact us!
