PIXIE
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is a Data Engineer Consultant for a 6-month contract at a pay rate of "X" per hour. Key skills include DBT development, SQL proficiency, data modeling, and version control with GitHub. Experience in ETL processes is required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
November 8, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#GitHub #Version Control #SQL (Structured Query Language) #dbt (data build tool) #Documentation #Data Transformations #Data Accuracy #Complex Queries #Data Pipeline #Data Engineering #"ETL (Extract #Transform #Load)" #Data Modeling
Role description
We are looking for a Data Engineer Consultant to develop and maintain efficient data pipelines that provide valuable insights. You will work with various tools and techniques to ensure data accuracy and readability.
Key Responsibilities:
DBT Development: Build data transformations using DBT.
Version Control: Use GitHub for managing code versions and collaboration.
SQL Proficiency: Write complex queries with Common Table Expressions (CTEs) to improve performance and clarity.
Data Modeling: Create dimensional models and dynamic tables for analytics.
Optimization: Enhance data pipeline efficiency and processing times.
Testing: Conduct tests to ensure data accuracy and reliability.
Documentation: Maintain clear documentation of processes and models for team reference.
We are looking for a Data Engineer Consultant to develop and maintain efficient data pipelines that provide valuable insights. You will work with various tools and techniques to ensure data accuracy and readability.
Key Responsibilities:
DBT Development: Build data transformations using DBT.
Version Control: Use GitHub for managing code versions and collaboration.
SQL Proficiency: Write complex queries with Common Table Expressions (CTEs) to improve performance and clarity.
Data Modeling: Create dimensional models and dynamic tables for analytics.
Optimization: Enhance data pipeline efficiency and processing times.
Testing: Conduct tests to ensure data accuracy and reliability.
Documentation: Maintain clear documentation of processes and models for team reference.
