SPECTRAFORCE
Senior GenAI Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior GenAI Data Engineer in San Francisco, CA (Hybrid). It offers an 8-month contract with a high possibility of extension. Key skills include GenAI proficiency, SQL mastery, and Python expertise. Experience in data integration and cloud platforms is preferred.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
April 1, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
Hybrid
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
San Francisco Bay Area
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🧠 - Skills detailed
#Observability #Snowflake #AWS (Amazon Web Services) #AI (Artificial Intelligence) #Databases #CRM (Customer Relationship Management) #GitHub #Pandas #BI (Business Intelligence) #Data Warehouse #Data Engineering #Data Integration #Cloud #dbt (data build tool) #Compliance #NumPy #Libraries #ML (Machine Learning) #GCP (Google Cloud Platform) #Python #"ETL (Extract #Transform #Load)" #Security #Data Science #Apache Airflow #Airflow #SQL (Structured Query Language) #GDPR (General Data Protection Regulation) #Azure
Role description
Title: Senior GenAI Data Engineer
Location: San Francisco, CA (Hybrid - 1-2 days per week)
Duration: 8 months assignment with high possibility of extension
What You’ll Do:
GenAI Engineering & Production
Data Engineering & Architecture
Business Intelligence & Stakeholder Management
Required Qualifications:
Technical Expertise:
GenAI Proficiency: Deep hands-on experience with LLM applications, including observability tools, evaluation frameworks, and safety guardrails
Agentic AI: Demonstrated experience building multi-agent or agentic workflows using LangGraph or similar frameworks
LLM Fundamentals: Strong understanding of how LLMs work, their capabilities and limitations, context windows, tokenization, embeddings, and fine-tuning
AI-Assisted Development: Active user of GenAI coding tools (Cursor, GitHub Copilot, Codex, Gemini Code Assist, etc.) with proven ability to accelerate development
SQL Mastery: Expert-level SQL skills including complex joins, window functions, CTEs, query optimization, and performance tuning
Data Engineering: Expert knowledge of dimensional modeling (star schemas, SCD Type 2), data warehouse concepts, and ETL/ELT patterns
Python Stack: Advanced proficiency in Python, pandas, numpy, and related data science libraries
Workflow Orchestration: Production experience with Apache Airflow or similar orchestration platforms
Enterprise Data Integration: Experience working with structured data from ERP, CRM, and financial systems
Nice to Have:
Experience with vector databases
Knowledge of cloud platforms (AWS, GCP, Azure) and their AI/ML services
Experience with dbt (data build tool) for analytics engineering
Experience with streaming data and real-time processing
Background in conversation intelligence or speech-to-text applications
Understanding of privacy, security, and compliance requirements for AI systems (SOC 2, GDPR, etc.)
Previous experience in a startup or fast-paced environment
Familiarity with modern data warehouse solutions (Snowflake, Hive)
Title: Senior GenAI Data Engineer
Location: San Francisco, CA (Hybrid - 1-2 days per week)
Duration: 8 months assignment with high possibility of extension
What You’ll Do:
GenAI Engineering & Production
Data Engineering & Architecture
Business Intelligence & Stakeholder Management
Required Qualifications:
Technical Expertise:
GenAI Proficiency: Deep hands-on experience with LLM applications, including observability tools, evaluation frameworks, and safety guardrails
Agentic AI: Demonstrated experience building multi-agent or agentic workflows using LangGraph or similar frameworks
LLM Fundamentals: Strong understanding of how LLMs work, their capabilities and limitations, context windows, tokenization, embeddings, and fine-tuning
AI-Assisted Development: Active user of GenAI coding tools (Cursor, GitHub Copilot, Codex, Gemini Code Assist, etc.) with proven ability to accelerate development
SQL Mastery: Expert-level SQL skills including complex joins, window functions, CTEs, query optimization, and performance tuning
Data Engineering: Expert knowledge of dimensional modeling (star schemas, SCD Type 2), data warehouse concepts, and ETL/ELT patterns
Python Stack: Advanced proficiency in Python, pandas, numpy, and related data science libraries
Workflow Orchestration: Production experience with Apache Airflow or similar orchestration platforms
Enterprise Data Integration: Experience working with structured data from ERP, CRM, and financial systems
Nice to Have:
Experience with vector databases
Knowledge of cloud platforms (AWS, GCP, Azure) and their AI/ML services
Experience with dbt (data build tool) for analytics engineering
Experience with streaming data and real-time processing
Background in conversation intelligence or speech-to-text applications
Understanding of privacy, security, and compliance requirements for AI systems (SOC 2, GDPR, etc.)
Previous experience in a startup or fast-paced environment
Familiarity with modern data warehouse solutions (Snowflake, Hive)
