Data Warehouse Modeler/Engineer

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

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