Sr Snowflake Data Engineer (On-site Interview)
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Sr Snowflake Data Engineer with a contract length of over 6 months, focusing on scalable data pipelines and data management. Key skills include Snowflake, Jenkins, GitHub, and experience in data warehousing and machine learning initiatives.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
September 2, 2025
🕒 - Project duration
More than 6 months
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🏝️ - Location type
Unknown
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
Houston, TX
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🧠 - Skills detailed
#Storage #Data Storage #GitHub #Snowflake #Data Science #Data Engineering #Data Warehouse #Data Lake #Automation #Data Pipeline #Jenkins #Scala #Data Management #Data Quality #ML (Machine Learning)
Role description
• Key Responsibilities:
1. Designing and implementing scalable data pipelines
1. Building and managing data warehouses and data lakes
1. Ensuring data quality and implementing data management best practices
1. Optimizing data storage and retrieval processes
1. Collaborate closely with data scientists, analysts, and product teams to support analytics and machine learning initiatives.
1. CI/CD orchestration and automation tools: Experience with tools such as Jenkins, GitHub etc.
1. Monitor and tune Snowflake query performance, warehouse usage, and credit consumption.
1. Collaborate closely with data scientists, analysts, and product teams to support analytics and machine learning initiatives.
1. Design and enforce row-level access policies and dynamic masking in Snowflake for sensitive data fields (PII, financials).
• Key Responsibilities:
1. Designing and implementing scalable data pipelines
1. Building and managing data warehouses and data lakes
1. Ensuring data quality and implementing data management best practices
1. Optimizing data storage and retrieval processes
1. Collaborate closely with data scientists, analysts, and product teams to support analytics and machine learning initiatives.
1. CI/CD orchestration and automation tools: Experience with tools such as Jenkins, GitHub etc.
1. Monitor and tune Snowflake query performance, warehouse usage, and credit consumption.
1. Collaborate closely with data scientists, analysts, and product teams to support analytics and machine learning initiatives.
1. Design and enforce row-level access policies and dynamic masking in Snowflake for sensitive data fields (PII, financials).
