Data Engineer

ABOUT THIS ROLE

As Data Engineer, AI, you will play a key role in building and advancing LVT's data platform — contributing across the full data engineering lifecycle, from ingestion and transformation to semantic modeling and delivery — while also helping build the data infrastructure that powers LVT's AI initiatives, including RAG pipelines and Snowflake Cortex models. This is a core engineering role with an AI edge: you'll keep the data platform running with precision and reliability, and you'll be the person who makes sure our AI systems have the clean, well-structured data they need to perform.

LVT is a flexible-first company. This role can be performed remotely, with a preference for candidates who can work from our American Fork, UT office.

ROLE RESPONSIBILITIES

• Design, build, and maintain scalable ELT pipelines that move data reliably from source systems into a clean, well-governed data platform.
• Develop and maintain semantic models that expose consistent, trusted business definitions across reporting and analytics surfaces.
• Own data quality — implement validation, monitoring, and alerting frameworks that catch problems before they reach stakeholders.
• Optimize Snowflake performance across ingestion, transformation, and storage, including dynamic tables, clustering, and query tuning.
• Architect and build the data infrastructure required for RAG applications, including vector storage, chunking strategies, and retrieval pipelines.
• Collaborate with AI/ML engineers to support Snowflake Cortex model development with well-structured, context-rich training and inference data.
• Partner with analytics engineers, data scientists, and business stakeholders to translate requirements into durable data solutions.
• Document data architecture, pipeline design, and modeling decisions to support team knowledge and long-term maintainability.

OUR IDEAL CANDIDATE

• Data Engineering Foundation: 5+ years building and maintaining production-grade pipelines using SQL, Python, and modern ELT tools such as dbt, Fivetran, or Airflow.
• Snowflake Proficiency: Deep hands-on experience with Snowflake, including performance optimization, dynamic tables, and familiarity with Cortex AI capabilities.
• Semantic Modeling Experience: Ability to design and maintain semantic or metrics layers that enforce consistent business logic across the organization.
• AI Infrastructure Awareness: Working knowledge of RAG architectures and the data patterns — chunking, embedding, retrieval — that support LLM-driven applications.
• Data Quality Ownership: Strong instinct for reliability — you instrument pipelines with observability and validation from the start.
• End-to-End Mindset: Comfortable owning work from scoping through production — you define the problem, build the solution, and stand behind the outcome.
• Collaborative Communicator: Able to work fluidly with engineers, analysts, and non-technical stakeholders, translating ambiguity into clear data solutions.