Data Engineer

Company Overview: Since 1953, Ferguson has been a source of quality supplies for a wide range of industries. Together, we build better infrastructure, better homes, & better businesses. We exist to make our customers complex projects simple, successful, & sustainable by proactively solving problems, adapting to change, & continuously improving how we serve our customers, communities, & each other.

Ferguson is a Fortune 500 company providing best-in-class products, services, & capabilities across multiple industries including Commercial/Mechanical, Facilities Supply, Fire & Fabrication, HVAC, Industrial, Residential Trade, Residential Building & Remodel, Waterworks, & Residential Digital Commerce. With approximately 36,000 associates across 1,700 locations, Ferguson is a community of people working toward a shared purpose of building something meaningful. Within Ferguson Enterprise, the Reporting & Analytics organization supports the business by developing scalable data & reporting solutions that help teams better understand performance & make informed decisions.

Our teams focus on building practical, high-quality analytics tools in a collaborative environment where technical excellence, ownership, & continuous improvement are valued. At Ferguson, you will have the opportunity to build a career you are proud of at a company you can believe in. Job Summary: The Data Engineer is a hands-on technical contributor responsible for developing & maintaining Power BI semantic models & analytical solutions that meet established technical & performance standards.

This role focuses on building clean, reliable, & well-structured reporting solutions using SQL, DAX, & Power BI while developing technical depth in data modeling & analytics practices. Associates work within defined requirements & contribute to team-based delivery through consistent, high-quality development & adherence to engineering standards. Essential Duties & Responsibilities: Write SQL queries to source & transform data from enterprise data platforms & curated reporting views applying star schema design principles & best-practice data modeling patterns.

Foundational understanding of Lakehouse architecture (Bronze, Silver, Gold) and querying curated datasets using Databricks SQL. Ability to connect Power BI to Databricks and understand basic performance considerations for large datasets. Awareness of data lineage and ability to validate data across upstream (Databricks) and downstream (Power BI) layers.

Familiarity with data quality validation and identifying inconsistencies in source data. Build, optimize & extend semantic models using Power BI datasets & Analysis Services. Develop DAX measures following established modeling & calculation standards.

Support data lineage documentation efforts to improve transparency into reporting dependencies. Contribute to the development & maintenance of shared datasets & reusable semantic models. Assist in documenting existing reports by identifying source tables, transformations, & measures used in reporting solutions.

Identify & escalate potential data quality issues observed within reporting datasets. Create & manage Row Level Security (RLS) role-based access. Participate in technical reviews & discussions.

Perform basic performance troubleshooting & DAX optimization. Utilize Python for basic data exploration, validation, & predictive analysis support. Contribute to building semantic models with clear and descriptive metadata to support AI-driven search & analytic use cases.

Skills & Qualifications: Bachelors degree in Computer Science, Information Systems, Data Analytics, or equivalent experience. Strong foundational understanding of SQL & DAX, Power BI OR Tableau 13 years of experience developing Power BI reports & datasets. Experience working with modern data platforms such as Databricks and querying data using Databricks SQL.

Understanding of Lakehouse architecture concepts, including bronze, silver, and gold data layers. Experience integrating Databricks data with Power BI semantic models (Import and DirectQuery). Familiarity with distributed data processing concepts and performance considerations for large-scale datasets.

Basic experience with Python or R for data analysis. Understanding of predictive analytics concepts such as regression & forecasting. Ability to work independently with defined technical requirements.

Understanding of best practices for AI-ready datasets, including semantic clarity, metadata definition, and standardized metrics.