Manager - Data Engineering

About Ferguson:

Since 1953, Ferguson has been a source of quality supplies for a variety of industries. Together We Build Better infrastructure, better homes and better businesses. We exist to make our customers complex projects simple, successful, and sustainable. We proactively solve problems, adapt and grow to continuously serve our customers, communities and each other. Ferguson is proud to provide best-in-class products, service and capabilities across the following industries: Commercial/Mechanical, Facilities Supply, Fire and Fabrication, HVAC, Industrial, Residential Trade, Residential Building and Remodel, Waterworks and Residential Digital Commerce. Ferguson has approximately 36,000 associates across 1,700 locations. Ferguson is a community of proud associates who operate with the shared purpose of building something meaningful. You will build a career that you are proud of, at a company you can believe in.

Job Summary:

The Data Engineering Manager is a working manager responsible for overseeing delivery execution for the development team while remaining actively engaged in technical work. This role manages task delegation, delivery progress, & team coordination while maintaining regular communication with US counterparts during overlap hours. The Manager ensures work is executed efficiently, technical standards are maintained, & team members are supported through coaching & problem-solving, while also contributing directly to development efforts when needed.

Essential Duties & Responsibilities:

Skills & Qualifications:

• Own delivery execution for the Ferguson Global Power BI development team. Lead initiatives to document reporting lineage, dataset dependencies, & metric definitions.
• Receive & translate incoming work requests into structured technical tasks & assign work across the team. Lead internal team ceremonies including standups, planning, & work tracking
• Manage delivery timelines, progress tracking, & quality assurance for reporting & semantic model development. Hold team members accountable for quality, deadlines, & technical standards.
• Review deliverables prior to release to ensure compliance with technical, performance, & design standards. prior to release to US teams. Maintain regular communication with US counterparts during overlap hours.
• Contribute to development efforts through hands-on Power BI, SQL, & DAX work as needed. Provide technical coaching & unblock developers when challenges arise.
• Ensure consistency in modeling practices, report design standards, & performance optimization techniques. Support advanced development & troubleshooting activities while fostering continuous improvement
• Establish standards & governance for building AI-ready datasets that enable natural language querying & AI-driven analytics across the organization.
• Drive adoption of AI-enabled analytics & capabilities by aligning semantic model design with Enterprise AI strategy.
• Bachelor's degree in Computer Science, Information Systems, Data Analytics, or equivalent experience.
• Expert-level Power BI development experience with strong SQL & DAX expertise.
• 8+ years of experience developing modern data platforms such as Databricks and querying data using Databricks SQL.
• Understanding Lakehouse architecture concepts with hands on development of bronze, silver, and gold data layers.
• Experience integrating Databricks data with Power BI semantic models (Import and Direct Query).
• Familiarity with distributed data processing concepts and performance considerations for large-scale datasets.
• Strong experience with Python or R for predictive analytics & advanced analysis.
• Proven experience mentoring developers & leading technical delivery teams.
• Strong organizational & delivery management skills.
• Ability to balance leadership responsibilities with hands-on development work.
• High accountability mindset for execution quality & team performance.
• Understanding of AI-enabled analytics platforms such as Azure OpenAI or Databricks and their dependency on well-structured semantic data models.