Data Modeler

FCS Software Solutions; United States; Remote; USD 100,000 - 130,000; Full time

Job summary

A leading company is seeking an experienced Data Modeler for their Enterprise Data Platform initiative. This remote role focuses on optimizing data models in BigQuery, requiring strong collaboration with data engineers and business analysts. Ideal candidates will have extensive experience in data modeling, particularly within the retail domain, and a solid understanding of cloud data platforms. Excellent communication skills are essential for translating business needs into structured data models.

Qualifications

• 7+ years of experience in data modeling and architecture.
• Expertise in dimensional modeling and data vault.

Responsibilities

• Design logical, conceptual, and physical data models for EDP.
• Define and maintain data definitions and relationships.
• Engage with business analysts to understand data needs.

Skills

Data Modeling

SQL

Communication

Tools

BigQuery

GCP

Tableau

Power BI

Job description

Job Title: Data Modeler Enterprise Data Platform (BigQuery, Retail Domain)

Location: Remote

Duration: Contract

Timing: US EST Hours

We have an immediate need for an IT Data Modeler Enterprise Data Platform (BigQuery, Retail Domain) reporting to Enterprise Data Platform (EDP) Architecture Team.

Job Summary:

We are seeking an experienced Data Modeler to support the Enterprise Data Platform (EDP) initiative, focusing on building and optimizing curated data assets on Google BigQuery. This role requires expertise in data modeling, strong knowledge of retail data, and an ability to collaborate with data engineers, business analysts, and architects to create scalable and high-performing data structures.

Key Responsibilities:

1. Data Modeling & Curated Layer Design

• Design logical, conceptual, and physical data models for EDPs curated layer in BigQuery.
• Develop fact and dimension tables, ensuring adherence to dimensional modeling best practices (Kimball methodology).
• Optimize data models for performance, scalability, and query efficiency in a cloud-native environment.
• Work closely with data engineers to translate models into efficient BigQuery implementations (partitioning, clustering, materialized views).

2. Data Standardization & Governance

• Define and maintain data definitions, relationships, and business rules for curated assets.
• Ensure data integrity, consistency, and governance across datasets.
• Work with Data Governance teams to align models with enterprise data standards and metadata management policies.

3. Collaboration with Business & Technical Teams

• Engage with business analysts and product teams to understand data needs, ensuring models align with business requirements.
• Partner with data engineers and architects to implement best practices for data ingestion and transformation.
• Support BI & analytics teams by ensuring curated models are optimized for downstream consumption (e.g., Looker, Tableau, Power BI, AI/ML models, APIs).

Required Qualifications:

• 7+ years of experience in data modeling and architecture in cloud data platforms (BigQuery preferred).
• Expertise in dimensional modeling (Kimball), data vault, and normalization/denormalization techniques.
• Strong SQL skills, with hands-on experience in BigQuery performance tuning (partitioning, clustering, query optimization).
• Understanding of retail data models (e.g., sales, inventory, pricing, supply chain, customer analytics).
• Experience working with data engineering teams to implement models in ETL/ELT pipelines.
• Familiarity with data governance, metadata management, and data cataloging.
• Excellent communication skills and ability to translate business needs into structured data models.
• Prior experience working in retail or e-commerce data environments.
• Exposure to modern data architectures (data lakehouse, event-driven data processing).
• Familiarity with GCP ecosystem (Dataflow, Pub/Sub, Cloud Storage) and BigQuery security best practices.

Thanks & Regards:

Abhinav Krishna Srivastava

Mob : [phone]

Email: [e-mail]