Cygnus Professionals Inc.
Big Data & Agentic AI Workflows
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role requires a Big Data & Agentic AI Workflows expert with 14+ years of experience, focused on data engineering, ETL pipelines, and AWS. Key skills include Python, SQL, Hadoop, and cloud data engineering. Contract length and pay rate are unspecified.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
August 14, 2026
🕒 - 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
Mountain View, CA
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🧠 - Skills detailed
#"ETL (Extract #Transform #Load)" #Big Data #Data Warehouse #S3 (Amazon Simple Storage Service) #Data Pipeline #Python #Spark (Apache Spark) #Redshift #EC2 #Cloud #Shell Scripting #Scripting #Data Mart #SQL (Structured Query Language) #Data Engineering #Hadoop #REST (Representational State Transfer) #Database Schema #Schema Design #AI (Artificial Intelligence) #Data Processing #JSON (JavaScript Object Notation) #AWS (Amazon Web Services)
Role description
⚠️14+ Years of experience.
Below are the evaluation guidelines for the role:
1. Data Engineering
• Hands-on experience creating and managing ETL/data pipelines
• Experience designing Data Warehouses and Data Marts
• Large-scale data processing experience
1. Technical Skills
• Python scripting
• Shell scripting
• SQL – SQL-based technical evaluation
• Hadoop – Hive on Spark
• AWS / cloud-based data engineering experience
1. AI / LLM
• Evaluate basic LLM/AI knowledge and understanding
• Advanced Agentic AI or complex LLM questions are not required
Must-have:
1. Strong Data Engineering / Big Data experience – building large-scale, fault-tolerant data platforms and pipelines.
1. Python or Shell – advanced scripting proficiency.
1. AWS – EC2, S3, EMR, Redshift, or equivalent cloud experience.
1. Hadoop ecosystem – specifically Hive and Hive on Spark.
1. ETL/ELT – hands-on pipeline development and database schema design.
1. Strong SQL – analytical SQL, data marts, data warehousing, and analytic architecture.
1. Agentic AI / LLM experience – practical experience using AI agents/LLM tools to automate parts of the Data Development Lifecycle.
1. Large-scale production data – experience processing high data volumes.
1. REST/JSON APIs – experience creating/consuming APIs and integrating systems.
1. Production/Operational ownership – SLA, incident, and problem management.
⚠️14+ Years of experience.
Below are the evaluation guidelines for the role:
1. Data Engineering
• Hands-on experience creating and managing ETL/data pipelines
• Experience designing Data Warehouses and Data Marts
• Large-scale data processing experience
1. Technical Skills
• Python scripting
• Shell scripting
• SQL – SQL-based technical evaluation
• Hadoop – Hive on Spark
• AWS / cloud-based data engineering experience
1. AI / LLM
• Evaluate basic LLM/AI knowledge and understanding
• Advanced Agentic AI or complex LLM questions are not required
Must-have:
1. Strong Data Engineering / Big Data experience – building large-scale, fault-tolerant data platforms and pipelines.
1. Python or Shell – advanced scripting proficiency.
1. AWS – EC2, S3, EMR, Redshift, or equivalent cloud experience.
1. Hadoop ecosystem – specifically Hive and Hive on Spark.
1. ETL/ELT – hands-on pipeline development and database schema design.
1. Strong SQL – analytical SQL, data marts, data warehousing, and analytic architecture.
1. Agentic AI / LLM experience – practical experience using AI agents/LLM tools to automate parts of the Data Development Lifecycle.
1. Large-scale production data – experience processing high data volumes.
1. REST/JSON APIs – experience creating/consuming APIs and integrating systems.
1. Production/Operational ownership – SLA, incident, and problem management.
