Sr Data Engineer

Lowe's Companies, Inc.•Charlotte, NC

10d•Onsite

About The Position

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.

Requirements

Bachelor’s degree in engineering, computer science, computer information systems (CIS), or related field or equivalent years of experience in lieu of education requirement, if applicable
5 years of experience in data, business intelligence or platform engineering, data warehousing/ETL, or software engineering
4 years of experience working on project(s) involving the implementation of solutions applying development life cycles (SDLC)
3 years of experience in object-oriented programming/structure programming, SQL, and scripting
3 years of experience in big data technology
3 years of experience in cloud-based big data technologies

Nice To Haves

Master’s degree in Business, Engineering, Computer Science, Data Science, Statistics, Information Systems, Economics, or related field.
Experience with semantic modeling, governed datasets, LookML, dbt, advanced SQL, and cloud data platforms.
Experience with data modeling, optimization, data quality, lineage, testing, CI/CD, and reporting automation.
Experience with self-service and AI-assisted analytics, metric governance, and tools such as Python, R, Alteryx, Knime, or SAS.
Experience delivering complex data solutions across Product, Engineering, Analytics, and business teams, including project management and large-scale retail data.

Responsibilities

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.
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.
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.
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.
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.