Data Engineer [T500-25697]

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.