Sr Data Engineer

Innovate in Charlotte The primary purpose of this role is to build the trusted analytical foundations that allow Lowe's teams to scale insights, reporting, self-service analytics, dashboards, and AI-assisted decision-making. The Senior Data engineer helps convert repeated business questions, metric definitions, and dashboard needs into reusable and governed analytical data products. This role plays a critical part in reducing manual data pulls, improving consistency of metrics, enabling self-service, supporting semantic layer development, and creating AI-ready data foundations. By building scalable and trusted analytical assets, this role allows analysts to spend more time on complex business analysis, root-cause analysis, storytelling, recommendations, and decision support. This role works closely with Analytics, Product, Engineering, Data Engineering, and business stakeholders to ensure that data models, semantic layers, explores, and metric definitions are accurate, documented, reusable, and aligned to business needs. Where You'll Be Associates are required to relocate to the Lowe's Tech Hub Charlotte NC to foster collaboration and support. The ideal candidate must be willing to work in the office at our Charlotte Tech Hub, office location, 5 days per week. Most business meetings are planned around the Eastern time zone Work with a Winning Team: As part of a Fortune 500 company and retail leader, your work can change an entire industry. Our CEO is a forward-thinker when it comes to tech, and with one of Forbes Top 50 CIOs leading the charge, you can come to work knowing you'll have access to the data, tools, and support that few other companies can offer. We also know what it takes to create an inclusive culture that supports you. Our teams are structured around the engineer, giving you the support you need to do your best work. Since we've been in business for over 100 years, we've built an excellent track record of growth and success. There's peace of mind knowing you have the stability and resources you need to focus on solving tough challenges. And as you solve these challenges, know you'll be surrounded by supportive associates with curious minds who listen to you, respect you, and recognize your hard work. What You Will Do 1. Analytical Data Modeling & Semantic Layer Translate business problems, reporting needs, and metric definitions into reusable analytical data models, semantic layer objects, explores, measures, dimensions, and certified datasets. Use advanced SQL, data modeling, and domain knowledge to build reliable analytical logic across enterprise data platforms. Develop governed semantic layer assets and self-service explores that reduce ad hoc data pulls and enable scalable reporting. 2. Data Engineering & Quality Design, build, validate, and maintain scalable analytical assets supporting dashboards, self-service reporting, AI-assisted analysis, and decision-making. Perform data validation, reconciliation, quality checks, and performance reviews to ensure accurate, consistent, and scalable outputs. Partner with development teams to test, deploy, monitor, and optimize analytical assets, semantic layer updates, tracking logic, and integrations. 3. Project Planning & Cross-Functional Collaboration Develop data engineering project plans covering scope, requirements, technical approach, dependencies, timelines, risks, and outcomes. Partner with Analytics, Product, Engineering, Data Engineering, and business teams to align definitions, source logic, data lineage, grain, joins, filters, attribution, and usage expectations. Clearly communicate data model design, semantic logic, project status, risks, issues, and recommendations to stakeholders. 4. Reporting Optimization & AI-Ready Analytics Support dashboard rationalization by identifying duplicate logic, low-value reporting, manual work, and opportunities to automate, productize, transition, or retire assets. Support Newton Analyst and AI-assisted analytics by validating outputs, enabling trusted metric access, and creating governed AI-ready data assets. Recommend improvements to data quality, metric consistency, self-service adoption, reporting scalability, and business impact. 5. Documentation, Mentorship & Impact Maintain documentation for metric definitions, transformation logic, source-to-target mapping, lineage, assumptions, limitations, and usage guidance. Mentor Associate Data Engineers, Analysts, and team members on SQL, data modeling, semantic layers, validation, documentation, and engineering best practices. Measure impact through reduced manual data pulls, improved dashboard performance, increased self-service adoption, stronger metric consistency, and greater trust in analytical outputs. Minimum Qualifications Bachelor's d #J-18808-Ljbffr Lowe's