TechnoSphere, Inc.
Lead Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Lead Data Engineer with 5-9 years of experience, focusing on Azure Cloud, MS Fabric, ADF, Azure SQL, and Synapse. Preferred experience in Data Mesh implementation and the insurance domain. Contract length and pay rate unspecified.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
April 9, 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
United States
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🧠 - Skills detailed
#ADF (Azure Data Factory) #SQL (Structured Query Language) #Data Science #Data Warehouse #Synapse #Batch #Strategy #Azure #Azure SQL #Data Modeling #dbt (data build tool) #Scala #Data Engineering #Data Pipeline #Azure cloud #Cloud
Role description
Job Description:
Experience level (Years): 5-9 years
Must have skills: Azure Cloud, MS Fabric, ADF, Azure SQL, Synapse, DBT
Preferred skills: Data Mesh implementation, Insurance domain
Job Description:
• Designs, builds, and maintains scalable data pipelines and architectures, leading a team to deliver robust data solutions.
• Drive the design, architecture, and implementation of robust, scalable data pipelines (batch/streaming) and data warehouses.
• Create efficient data models and manage data modeling standards for structured and unstructured data.
• Optimize data systems and pipelines for performance, scalability, and cost-efficiency; resolve data-related issues.
• Work with stakeholders (Data Scientists, Product Managers) to understand data requirements.
• Define the strategy for adopting modern technologies and cloud platforms.
Job Description:
Experience level (Years): 5-9 years
Must have skills: Azure Cloud, MS Fabric, ADF, Azure SQL, Synapse, DBT
Preferred skills: Data Mesh implementation, Insurance domain
Job Description:
• Designs, builds, and maintains scalable data pipelines and architectures, leading a team to deliver robust data solutions.
• Drive the design, architecture, and implementation of robust, scalable data pipelines (batch/streaming) and data warehouses.
• Create efficient data models and manage data modeling standards for structured and unstructured data.
• Optimize data systems and pipelines for performance, scalability, and cost-efficiency; resolve data-related issues.
• Work with stakeholders (Data Scientists, Product Managers) to understand data requirements.
• Define the strategy for adopting modern technologies and cloud platforms.
