Data Modeler / Data Modeling Lead, 100% Remote

Tror; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

Tror is seeking a Senior Data Modeler / Data Modeling Lead to guide enterprise data modeling and provide technical leadership across data engineering, Snowflake, dbt, and data warehouse architecture.

You will own data modeling across Bronze, Silver, and Gold layers, design conceptual, logical, and physical models, build Data Vault 2.0 models, and drive data quality, lineage, and CI/CD practices for analytics workloads in a 100% remote environment.

Qualifications

• Senior data modeling leader for enterprise datasets.
• Ability to guide cross-team data engineering initiatives.
• Experience with dimensional modeling and Data Vault 2.0 is required.

Responsibilities

• Lead data modeling across Bronze, Silver, and Gold layers.
• Create conceptual, logical, and physical data models.
• Design dimensional models (Kimball) and Data Vault 2.0 structures.
• Define conformed dimensions and business entities used across teams.
• Establish modeling standards, naming conventions, and data lineage.
• Review source-to-target mapping documents and ensure data quality.
• Guide CI/CD for data and analytics workloads.

Skills

Data modeling

Dimensional modeling

Data Vault 2.0

SQL

ETL/ELT architecture

Data lineage

CI/CD for data

Tools

Snowflake

dbt

Job description

Role: Data Modeler / Data Modeling Lead

Location: 100% Remote

Role Overview

Looking for a Senior Data Modeler / Data Modeling Lead who can lead enterprise data modeling and also provide technical leadership across data engineering, Snowflake, dbt, and data warehouse architecture.

The person should be strong in data modeling, dimensional modeling, Data Vault 2.0, Snowflake, SQL, dbt, and ETL/ELT architecture.

Key Responsibilities

1. Data Modeling

• Lead data modeling across Bronze, Silver, and Gold layers.
• Create Conceptual, Logical, and Physical Data Models.
• Design Dimensional Models / Kimball models.
• Build and maintain Data Vault 2.0 models.
• Define common/conformed dimensions and business entities used across different teams.
• Establish data modeling standards, naming conventions, and best practices.
• Review Source-to-Target Mapping (STTM) documents.
• Ensure proper data lineage from source systems to target tables.
• Standardize and harmonize data across different business domains.

2. Data Engineering & Architecture

• Lead the design of scalable data ingestion and transformation frameworks.
• Design ELT pipelines using Snowflake and dbt.
• Design integration patterns for:
• Batch processing
• CDC (Change Data Capture)
• Near real-time data
• Ensure data pipelines are scalable, reliable, and optimized for performance.
• Establish frameworks for:
• Data quality
• Data reconciliation
• Data auditing
• Data monitoring
• Guide CI/CD implementation for data and analytics workloads.
• Work with engineering teams to ensure best practices are followed.