Overview
On Site
$60,000 - $80,000
Full Time
Skills
Snowflake
SQL
Python & Java
AWS & Azure
ETL Pipelines
Data Governance
Data Modeling
DWH
Data Analysis
Big Data
ML
Spark & Tensorflow
Job Details
Technical Skills:
- SQL:Proficiency in writing and optimizing SQL queries is essential for data manipulation and analysis within Snowflake.
- Programming Languages (Python, Java, etc.):Experience with Python and other languages allows for building custom ETL pipelines, data processing scripts, and integration with other systems.
- Cloud Computing:Understanding of cloud platforms (AWS, Azure, Google Cloud Platform) is crucial for working with Snowflake and managing data in the cloud.
- ETL Tools:Familiarity with various ETL tools (e.g., dbt or Airflow) is needed for extracting, transforming, and loading data into Snowflake.
- Data Modeling:The ability to design robust data models (e.g., star schemas, snowflake schemas) that meet business needs and optimize Snowflake performance is essential.
- Snowflake Architecture & Tools:Understanding Snowflake's unique architecture, including its shared data and compute layers, and its various tools (e.g., Snowpark, dbt) is crucial.
- Data Warehousing Concepts:Solid understanding of data warehousing principles, data lakes, and data dictionaries is needed for building and managing data pipelines within Snowflake.
- Data Analysis:Ability to analyze data, identify trends, and provide insights to stakeholders.
- Big Data Technologies:Knowledge of big data technologies like Hadoop and Kafka can be beneficial for handling large datasets and streaming data.
- Machine Learning (Basic):Basic knowledge of machine learning principles and frameworks (e.g., TensorFlow, Spark) can be useful for data preparation and model building within Snowflake.
Soft Skills:
Communication & Collaboration:
Ability to communicate technical concepts clearly and work effectively with other teams (e.g., data scientists, business analysts) is crucial.
Business Acumen:
Understanding of business needs and objectives is essential for designing and implementing data pipelines that support business goals.
Project Management:
Experience with project management methodologies and tools can be helpful for managing data engineering projects
Innovation & Continuous Learning:
A willingness to learn and adopt new technologies and techniques is important for staying up-to-date with the rapidly evolving data landscape.
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