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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** LVT LLC - **Location:** American Fork, UT, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Business Logic, ARM Architecture, Encodings, Information Engineering, Data Infrastructure, Data Security, Data Systems, Python (Programming Language), Operational Databases, Performance Tuning, SQL Databases, AI Infrastructure, Snowflake, Data Pipelines - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/senior-data-engineer-lvt-8008972 ## About the Role * Seasoned Data Engineer: 7+ years building and operating production data pipelines using SQL, Python, and modern ELT tooling (dbt, Fivetran, Airflow, or equivalents). You've owned systems under pressure and know how to build for long-term reliability. * Snowflake Depth: Expert-level Snowflake experience-performance optimization, dynamic tables, data security, and Cortex familiarity. You know when and why to use each capability. * Semantic Modeling Ownership: Proven ability to design and maintain semantic or metrics layers that enforce consistent business logic across a complex, multi-team organization. * High-Impact Execution: You operate at the level of organizational strategy-your work influences competitive positioning, not just sprint delivery. You define standards, evaluate trade-offs, and build for the long term. * Data Quality Obsession: Reliability is non-negotiable. You instrument pipelines with observability, validation, and alerting from day one, and you define the standards others follow. * Leadership & Ownership: You own work from scoping through production and beyond. You influence company-wide technology decisions and mentor others along the way-without waiting to be told what to do. * AI Infrastructure Fluency: Familiar with AI/ML data patterns-RAG architectures, vector stores, embedding pipelines-and able to build the data infrastructure those systems require. * Executive Communication: You build partnerships with executives and cross-functional stakeholders, translating complex technical trade-offs into clear recommendations that earn trust and drive adoption., LVT IS PROUD TO BE AN EQUAL OPPORTUNITY EMPLOYER. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. All candidates must pass a drug screening and background check upon employment. Some roles may also require passing a federal background check and fingerprinting. Must be authorized to work in the U.S. If reasonable accommodation is needed to participate in the job application or interview process, and/or to perform essential job functions, please reach out to your recruiter. ## Description * Design, build, and maintain scalable, production-grade ELT pipelines that move data reliably from diverse source systems into a clean, well-governed data platform. * Architect and own LVT's Snowflake environment-performance tuning, dynamic tables, clustering strategies, storage optimization, and cost governance. * Develop and enforce semantic models that expose consistent, trusted business definitions across all reporting and analytics surfaces. * Define and drive data engineering standards that improve quality, reliability, and productivity across teams-not just within BI. * Lead cross-functional data initiatives, partnering with engineering, finance, operations, and product to deliver solutions that drive meaningful organizational outcomes. * Establish data quality infrastructure-implement validation, monitoring, and alerting frameworks that surface problems before they reach stakeholders. * Contribute to AI data infrastructure-support RAG pipelines, vector storage, and Snowflake Cortex integrations as one component of the broader engineering scope. * Mentor junior engineers and build strong partnerships with executives and business stakeholders to drive adoption of data solutions. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [A Brief History of Data Storage](https://www.wearedevelopers.com/videos/974-a-brief-history-of-data-storage) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)