Data Warehouse Modeler/Engineer

Role: Data Warehouse Modeler/Engineer
Location Mode: On-site 5 days a week (Basildon or Dublin)
Employment Type: Full-Time Employement (FTE)

Job Overview

We are seeking a highly skilled Data Warehouse Modeler & Engineer (dbt & Snowflake) to join our EMEA Data Solutions domain. In this role, you will be key to designing and implementing robust, end-to-end data integration and transformation solutions on Snowflake. This position blends ETL/ELT architecture, data modeling, data mapping, and hands-on dbt development.

Key Responsibilities

• Architect and build ETL/ELT pipelines to support data ingestion, transformation, and consolidation.
• Map source system data into target Snowflake structures effectively.
• Design and maintain conceptual, logical, and physical data models (covering staging, integration layers, and data marts).
• Develop, test, and manage production-ready dbt models adhering to industry best practices.
• Fine-tune Snowflake performance through efficient query design, materialization strategies, and clustering.
• Uphold high standards of data quality, consistency, data lineage, and documentation.
• Partner with analytics, engineering, and platform teams to establish and align on data standards.

Technical Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related technical field.
• 7+ years of experience in data engineering or modeling within a fast-paced, enterprise-scale environment.
• Strong mastery of SQL alongside practical experience with dbt (Core/Cloud) or traditional ETL tools (Informatica, Talend, ODI, SSIS, etc.).
• Hands-on expertise in Snowflake data engineering.
• Solid background in SQL-based data modeling methodologies (Kimball, Inmon, Data Vault) and full ETL/ELT pipeline design.
• Deep comprehension of modern end-to-end data ingestion and transformation patterns.
• Proficiency in data quality assurance, data governance, and query optimization.
• Nice to have: Familiarity with the AWS data ecosystem (S3, Glue, EMR/PySpark, Apache Iceberg).

Non-Technical Skills & Competencies

• Proven sense of ownership, commitment, and personal accountability.
• Meticulous attention to detail with a proactive problem-solving mindset.
• Keen interest in novel data technologies and continuous innovation.
• Clear, effective verbal and written communication abilities.
• Experience leveraging AI-powered tools and workflows.

Skills Breakdown

Mandatory Skills: TOAD Data Modeler
Good to Have Skills: Data Modeling