The data engineering world has changed dramatically in the last five to ten years. A decade ago, “data engineer” often meant someone using drag-and-drop tools like Informatica to string together boxes and lines ,or a business intelligence analyst who happened to understand how data needed to move. Today, most data engineers write code. The Venn diagram between data engineering and software engineering has never been tighter.
The consulting gap
Traditional consulting firms approach data engineering the same way they approach every other practice: provide thought leadership, solve high-level problems, and move on. The day-to-day rigor of how work actually gets delivered is often an afterthought. Feedback loops are long.
We want to do something different. Our goal is to apply the same software engineering discipline we bring to our development teams to the way we deliver data work.
What “software-style” delivery looks like in practice
This means a few things:
- Actionable Agile: We use structured delivery frameworks to create visibility, unblock engineers quickly, and forecast deliverables, not just check boxes on a project plan.
- Technical depth: Our engineers are fluent in the architecture they work with. That means understanding the internals of tools like Apache Airflow, knowing how Snowflake models and moves data, and having real opinions about when a native AWS tool is the right call versus when a managed warehouse is better.
- Strong schema enforcement and data quality: One of the most common failure modes we see in data systems is weak schema discipline. We treat data quality with the same seriousness as functional correctness in software.
Why the foundation matters
Here’s the thing most people don’t say out loud: your data quality is only as good as your software engineering. If the systems generating your data are poorly built, no amount of downstream tooling fixes it. Strong data engineering starts upstream. That’s why we bring the same rigor to data work that we bring to every line of code we write.
Ready to build data systems that actually hold up?
If your team is dealing with brittle pipelines, inconsistent data, or a platform that’s grown faster than the engineering discipline around it, we’d like to talk. Get in touch with our engineering team.
