Snow Flake Data Engineer

Overview

On Site
$60,000 - $80,000
Full Time

Skills

Snowflake
Data Warehousing
SQL
ETL/ELT
AWS & Azure
Data Modeling
Performance Tuning
Python
API
Data Integration & Migration
Data Governance & Security
Data Pipelines & Workflow Automation
Data Quality Assurance
BI Tools
ML
Project Management
Data Lakes
Data Virtualization
Data Mesh

Job Details

Technical Skills:
  • Snowflake Expertise:
    This includes a deep understanding of Snowflake's architecture, features, and functionalities like Snowpipe, Stored Procedures, Streams, and Tasks.
  • SQL Proficiency:
    Snowflake is a SQL-based platform, so strong SQL skills are crucial for querying, data manipulation, and performance optimization.
  • Data Warehousing Concepts:
    Understanding data warehousing principles, including data modeling, schema design, and dimensional modeling, is essential for designing efficient data storage and retrieval.
  • ETL/ELT Processes:
    Snowflake Data Engineers need to be proficient in designing and implementing ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes to move data into Snowflake from various sources.
  • Cloud Computing:
    Experience with cloud platforms like AWS, Azure, or Google Cloud Platform is often required, as Snowflake is a cloud-based data warehouse.
  • Data Modeling:
    The ability to design robust data models that support business requirements, including creating schemas, defining relationships, and ensuring data integrity.
  • Performance Tuning:
    Optimizing data pipelines and queries for performance and scalability using Snowflake's features and best practices.
  • Scripting Languages:
    Proficiency in scripting languages like Python is important for automating tasks, developing data pipelines, and interacting with Snowflake's API.
  • Data Integration and Migration:
    Experience with integrating Snowflake with other systems and migrating data from various sources into Snowflake.
  • Data Governance and Security:
    Understanding data governance principles and implementing security measures to protect sensitive data within Snowflake.
  • Data Pipelines and Workflow Automation:
    Designing, building, and managing data pipelines using tools like Airflow, Azure Data Factory, or other workflow automation solutions.
  • Data Quality Assurance:
    Implementing measures to ensure the accuracy, completeness, and reliability of data within Snowflake.
  • BI Tools:
    Familiarity with BI tools for data visualization and analysis, such as Tableau, Power BI, or Looker, can be beneficial.
  • Machine Learning:
    Snowflake's capabilities for handling large datasets and integrating with machine learning frameworks can be leveraged by data engineers.
Soft Skills:
  • Problem-Solving:
    Snowflake Data Engineers need strong problem-solving skills to troubleshoot issues, optimize performance, and design effective solutions.
  • Communication:
    Clear and effective communication skills are essential for collaborating with stakeholders, explaining technical concepts, and documenting solutions.
  • Teamwork:
    The ability to collaborate effectively with other team members, including data analysts, data scientists, and other engineers.
  • Project Management:
    Some Snowflake Data Engineer roles may require project management skills to plan, execute, and deliver projects on time and within budget.
  • Continuous Learning:
    The field of data engineering is constantly evolving, so Snowflake Data Engineers need to be adaptable and willing to learn new technologies and techniques.
Other Useful Skills:
  • DevOps Practices:
    Understanding DevOps principles and using tools for continuous integration and continuous delivery (CI/CD) can be beneficial.
  • Data Lakes:
    Familiarity with data lake concepts and how they integrate with Snowflake for handling unstructured and semi-structured data.
  • Data Virtualization:
    Understanding how to virtualize data from different sources and make it available within Snowflake.
  • Data Mesh:
    Understanding the principles of data mesh architecture and how it can be implemented with Snowflake.
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