

Pegasus Knowledge Solutions, Inc.
Mid/Senior Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Mid/Senior Data Engineer with a contract length of "unknown" and a pay rate of "unknown." Key skills include 5+ years in data engineering, proficiency in Python and PySpark, and experience with vector databases.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
July 15, 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
New York, United States
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🧠 - Skills detailed
#Spark (Apache Spark) #Data Pipeline #AI (Artificial Intelligence) #Data Warehouse #Big Data #"ETL (Extract #Transform #Load)" #Python #Storage #Jira #Databases #Presto #Cloud #Data Engineering #PySpark #Hadoop #Kafka (Apache Kafka)
Role description
We're transforming our customer support data from static, disconnected reports into a live, AI-powered system. We're looking for an experienced Data Engineer to build the pipelines, storage, and logic that power our new AI agents — turning messy customer chats and tickets into clean, structured information our AI can reliably use.
Job Responsibilities
• Ingest and clean large volumes of unstructured support data from Bliss, Salesforce, Sprinklr, and JIRA
• Design storage and retrieval systems (including vector databases) that give our AI accurate, relevant context
• Build a centralized, reliable metrics layer for AI-driven analytics
• Build robust, monitored, failure-resistant pipelines — no fire drills
• Partner with ops, product, and engineering; push back on poor data practices at the source
Required Skills
• 5+ years in data engineering with big data tools (Hadoop, Hudi, Spark, Presto, Pinot, Flink, Kafka) and cloud data warehouses
• Strong hands-on Python and PySpark
• Proven use of AI tools (e.g., Claude, Codex) to accelerate development — automating checks, parsing messy text, building data logic layers
• Direct experience with vector databases (Pinecone, Milvus, Weaviate, pgvector) and AI-feeding data pipelines
• Bonus: experience building metric or semantic layers
If this opportunity aligns with your experience and interests, I'd be happy to discuss it further. Please send your updated resume to sjain@pksi.com.
We're transforming our customer support data from static, disconnected reports into a live, AI-powered system. We're looking for an experienced Data Engineer to build the pipelines, storage, and logic that power our new AI agents — turning messy customer chats and tickets into clean, structured information our AI can reliably use.
Job Responsibilities
• Ingest and clean large volumes of unstructured support data from Bliss, Salesforce, Sprinklr, and JIRA
• Design storage and retrieval systems (including vector databases) that give our AI accurate, relevant context
• Build a centralized, reliable metrics layer for AI-driven analytics
• Build robust, monitored, failure-resistant pipelines — no fire drills
• Partner with ops, product, and engineering; push back on poor data practices at the source
Required Skills
• 5+ years in data engineering with big data tools (Hadoop, Hudi, Spark, Presto, Pinot, Flink, Kafka) and cloud data warehouses
• Strong hands-on Python and PySpark
• Proven use of AI tools (e.g., Claude, Codex) to accelerate development — automating checks, parsing messy text, building data logic layers
• Direct experience with vector databases (Pinecone, Milvus, Weaviate, pgvector) and AI-feeding data pipelines
• Bonus: experience building metric or semantic layers
If this opportunity aligns with your experience and interests, I'd be happy to discuss it further. Please send your updated resume to sjain@pksi.com.






