Why This Role Stands Out
Leverage your expertise in data modeling for a hybrid role at SoftPath Technologies, focusing on innovative Lakehouse architecture with competitive hourly pay. This opportunity is ideal for experienced data modelers with strong finance domain knowledge seeking to shape best practices and drive impactful solutions. Apply now to advance your career in a flexible and rewarding environment.
Quick Overview
Job Description
Experience: 6+ Years
Start Date: 10/12/2026
End Date: 12/18/2026
Pay Rate: $70/Hour on W2
Position Type: Full-Time / Contract
Location: Remote (Chicago Preferred)
Duration: 6+ Months
Openings: 1
Job Description
We are looking for an experienced Data Modeler to design and develop logical and physical data models for the Silver and Gold layers of a Lakehouse architecture using Snowflake. The ideal candidate will have strong Finance domain expertise, particularly in General Ledger and Trial Balance data modeling.
Key Responsibilities
- Develop logical and physical data models for Silver and Gold layers of a medallion data warehouse architecture.
- Assess existing data models for efficiency and fitness for use.
- Lead client workshops to understand current-state challenges and define future-state Finance data models.
- Establish data modeling best practices for each layer of the medallion architecture.
- Provide thought leadership and recommendations for Finance data modeling.
- Work with functional teams to ensure models are scalable and extensible.
Required Skills & Qualifications
- Strong experience in Finance data modeling, especially General Ledger and Trial Balance.
- Experience modeling data from SAP, Oracle, and JD Edwards.
- Proficiency with data modeling tools and techniques.
- Strong understanding of 3NF, Star Schema, and Snowflake Schema.
- Experience developing Silver and Gold layer data models.
- Ability to define and implement data modeling best practices.
- Strong communication skills and experience leading client working sessions.
- Familiarity with Snowflake is preferred.
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