

Nasscomm
W2 Only|Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is a W2 Data Engineer for a 6+ month remote contract, requiring strong SQL skills and experience with data lakes or analytics. Responsibilities include improving Unity Catalog documentation and analyzing data pipelines. Familiarity with data governance and AI validation is preferred.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
July 17, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
Remote
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Data Pipeline #Data Quality #AI (Artificial Intelligence) #"ETL (Extract #Transform #Load)" #Data Engineering #Metadata #Documentation #Datasets #SQL (Structured Query Language) #Data Governance #Data Lake
Role description
Role: Data Engineer
Location: Remote 100%
Duration: 6+ Months Contract
Role focus:
This person would review and improve our data lake documentation in Unity Catalog, trace the upstream data pipelines that generate the data, and update business-friendly definitions so the documentation reflects how the data is actually created and used.
Core responsibilities:
• Review existing Unity Catalog table and field documentation and identify gaps, inaccuracies, and missing business context
• Analyze upstream data pipelines and SQL logic to understand how data is sourced, transformed, and published
• Document approved data use cases, including how specific datasets should be used for reporting, analysis, and AI/LLM scenarios
• Document data restrictions, including sensitive data considerations, inappropriate use cases, and areas where definitions or quality are not strong enough for broad agent or LLM use
• Help identify and structure use cases where LLMs or agents could responsibly interact with the data
• Test LLM responses against documented definitions and use cases to evaluate whether outputs are accurate, safe, and grounded in the intended business context
• Flag gaps in metadata, definitions, lineage, data quality, and ownership that must be addressed before wider AI enablement
Skills needed:
• Strong SQL skills
• Experience working with data lake, warehouse, or analytics environments
• Ability to read and reason through data pipelines and transformation logic
• Strong documentation skills, especially translating technical logic into business-friendly definitions
• Comfortable leading discovery conversations with technical and business stakeholders
• Familiarity with data governance, data quality, semantic definitions, and responsible AI / LLM validation is strongly preferred
Role: Data Engineer
Location: Remote 100%
Duration: 6+ Months Contract
Role focus:
This person would review and improve our data lake documentation in Unity Catalog, trace the upstream data pipelines that generate the data, and update business-friendly definitions so the documentation reflects how the data is actually created and used.
Core responsibilities:
• Review existing Unity Catalog table and field documentation and identify gaps, inaccuracies, and missing business context
• Analyze upstream data pipelines and SQL logic to understand how data is sourced, transformed, and published
• Document approved data use cases, including how specific datasets should be used for reporting, analysis, and AI/LLM scenarios
• Document data restrictions, including sensitive data considerations, inappropriate use cases, and areas where definitions or quality are not strong enough for broad agent or LLM use
• Help identify and structure use cases where LLMs or agents could responsibly interact with the data
• Test LLM responses against documented definitions and use cases to evaluate whether outputs are accurate, safe, and grounded in the intended business context
• Flag gaps in metadata, definitions, lineage, data quality, and ownership that must be addressed before wider AI enablement
Skills needed:
• Strong SQL skills
• Experience working with data lake, warehouse, or analytics environments
• Ability to read and reason through data pipelines and transformation logic
• Strong documentation skills, especially translating technical logic into business-friendly definitions
• Comfortable leading discovery conversations with technical and business stakeholders
• Familiarity with data governance, data quality, semantic definitions, and responsible AI / LLM validation is strongly preferred





