Sharp Decisions
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer on a contract-to-hire or full-time basis, requiring strong Python and Informatica skills, ETL experience, and familiarity with AI/ML concepts. Finance domain experience is preferred. Work is hybrid, with 2 days onsite.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
January 16, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
Hybrid
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📄 - Contract
Fixed Term
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🔒 - Security
Unknown
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📍 - Location detailed
New York, NY
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🧠 - Skills detailed
#Data Engineering #Informatica #AI (Artificial Intelligence) #"ETL (Extract #Transform #Load)" #Data Modeling #Data Science #Python #Data Pipeline #Snowflake #ML (Machine Learning)
Role description
Role: Data Engineer
Location: 2 days onsite (hybrid)
Type: Contract-to-Hire or Full-Time
Domain: Finance preferred, not mandatory
Key Skills & Requirements:
• Must-have: Strong Python and Informatica experience
• AI/ML Exposure: Familiarity with building and fine-tuning models; not a heavy AI/ML developer role
• ETL & Data Engineering: Hands-on experience with ETL pipelines, data modeling, and “data plumbing”
• Nice-to-have: Snowflake ecosystem experience, HR systems exposure
• Ability to collaborate with business teams handling Data Science
Candidate Profile:
• Hands-on Data Engineer comfortable building data pipelines and models
• Exposure to AI/ML concepts without being a full AI specialist
• Finance domain experience is a plus
Role: Data Engineer
Location: 2 days onsite (hybrid)
Type: Contract-to-Hire or Full-Time
Domain: Finance preferred, not mandatory
Key Skills & Requirements:
• Must-have: Strong Python and Informatica experience
• AI/ML Exposure: Familiarity with building and fine-tuning models; not a heavy AI/ML developer role
• ETL & Data Engineering: Hands-on experience with ETL pipelines, data modeling, and “data plumbing”
• Nice-to-have: Snowflake ecosystem experience, HR systems exposure
• Ability to collaborate with business teams handling Data Science
Candidate Profile:
• Hands-on Data Engineer comfortable building data pipelines and models
• Exposure to AI/ML concepts without being a full AI specialist
• Finance domain experience is a plus
