Maven Companies Inc.
Senior Data Modeler
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Modeler on a long-term contract in Orlando, FL (Hybrid). Requires 10+ years in IT, expertise in Erwin, Data Warehouse, SQL, and Oracle, with data engineering experience in Big Data and PySpark as a plus.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
May 2, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Hybrid
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Orlando, FL
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🧠 - Skills detailed
#Oracle #Metadata #Spark (Apache Spark) #PySpark #ERWin #Data Engineering #Physical Data Model #Conceptual Data Model #SQL (Structured Query Language) #Data Warehouse
Role description
Senior Data Modeler
Location-Orlando, FL(Hybrid)
Job Type-Long Term Contract
Must have at least 10+ years of IT Experience
• Erwin, Data Warehouse, Retail, SCM, SQL, Oracle and any Data Engineering experience Bigdata, Pyspark/Spark is a plus
• Analyzing and translating business needs into long-term solution data models.
• Evaluating existing data systems.
• Working with the development team to create conceptual data models and data flows.
• Developing best practices for data coding to ensure consistency within the system.
• Reviewing modifications of existing systems for cross-compatibility.
• Implementing data strategies and developing physical data models.
• Updating and optimizing local and metadata models.
• Evaluating implemented data systems for variances, discrepancies, and efficiency.
• Troubleshooting and optimizing data systems.
Senior Data Modeler
Location-Orlando, FL(Hybrid)
Job Type-Long Term Contract
Must have at least 10+ years of IT Experience
• Erwin, Data Warehouse, Retail, SCM, SQL, Oracle and any Data Engineering experience Bigdata, Pyspark/Spark is a plus
• Analyzing and translating business needs into long-term solution data models.
• Evaluating existing data systems.
• Working with the development team to create conceptual data models and data flows.
• Developing best practices for data coding to ensure consistency within the system.
• Reviewing modifications of existing systems for cross-compatibility.
• Implementing data strategies and developing physical data models.
• Updating and optimizing local and metadata models.
• Evaluating implemented data systems for variances, discrepancies, and efficiency.
• Troubleshooting and optimizing data systems.
