We work in analytics and data engineering, primarily around Power BI, Microsoft Fabric, and the systems that feed them. Over time, we’ve learned that the hardest part of analytics is rarely the visuals — it’s everything underneath: how data is shaped, which assumptions are embedded in the model, and whether the system holds up once real users start asking real questions.

Most of our work happens outside of clean, greenfield scenarios. Data is often messy, sources weren’t designed with analytics in mind, and requirements evolve as soon as insights start to surface. Much of this work is covered by NDAs, so rather than showcasing logos, the focus stays on building solutions that quietly do their job and keep doing it.

Our approach is deliberately straightforward. We spend time on the semantic layer and the data model before worrying about presentation. When the model is sound, reports tend to age well and adapt more easily. When it isn’t, no amount of visual refinement compensates. We aim for architectures that are understandable, maintainable, and proportionate to the problem being solved.

In practice, that usually means working with Power BI, Fabric, Azure SQL, SSIS, and Python, and choosing between ODS-style or dimensional models based on constraints rather than ideology. We have opinions, but we’re not dogmatic — trade-offs are inevitable, and making them explicit is part of the job.

We also believe strongly in building systems that can be owned by the teams who inherit them. Clear naming, documentation, and explainability matter. An analytics solution that depends on a single person’s context is fragile, even if it works today.

This site is where we share how we think about these problems: modeling decisions, performance and scale issues, governance friction, Python inside Power BI, and the practical realities of working with Fabric. It’s less about promotion and more about documenting what actually holds up in day-to-day work.

If you’re looking for loud promises or flashy dashboards, this may feel understated. If you value clarity, durability, and analytics that people trust and reuse, you’re in the right place.