ironquill.tech/board

$ cat jobs/us-lbm-lead-data-engineer-us-lbm-2998b56fdbbc.json

US LBM Lead Data Engineer

us lbm·US·United States·senior
snowflakeazuredata engineeringdevopsci/cd
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US LBM is one of the leading and fastest growing distributors of specialty building materials in the United States, with a team of over 13,000 employees located throughout the country. Since our founding in 2009, we have acquired over 100 companies and have expanded to more than 450 locations serving 37 states. US LBM is a progressive organization that promotes a unique culture that focuses on the value of its customers and associates. Developing our people is critical to our strategy and fostering our culture of empowerment. . A Brief Overview The US LBM Lead Data Engineer will have deep expertise in Snowflake, data architecture, and cloud-native data engineering to lead the design and delivery of scalable, high-performance data solutions. This role combines hands-on technical leadership with architectural oversight — guiding a team of data engineers to build robust data pipelines, APIs, and integrations that power analytics, AI, and business intelligence platforms. What you will do Define and evolve the enterprise data architecture strategy leveraging Snowflake, cloud-native design, and scalable frameworks. Lead and mentor a team of data engineers, providing technical direction, code reviews, and architectural oversight. Collaborate with enterprise and solution architects to ensure alignment with governance, security, and compliance standards. Design, build, and optimize ETL/ELT pipelines in Matillion and Snowflake for structured and semi-structured data. Implement and maintain data quality, lineage, and observability frameworks to ensure accuracy and reliability. Architect and deploy APIs for enterprise data access, integrating CI/CD and DevOps automation. Orchestrate data workflows using Snowpark, Azure Functions, API Gateways, and event-driven processing tools. Integrate AI and Cortex Services to enable intelligent data access, ML-driven insights, and scalable MLOps practices. Translate business needs into scalable technical solutions through close partnership

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