Industrial IoT Analytics for an Engineering Firm
Industrial IoT analytics delivered by an embedded data engineering team: Grafana dashboards on N3uron telemetry and SQL Server, built inside Greyhound Engineering's analytics practice.
- Geography
- Spain (with UK ties)
- Year
- 2025
- Stage
- Established engineering consultancy
- Team
- 1 data engineer + 1 tech lead
- Duration
- Ongoing strategic staff augmentation
The situation
Greyhound Engineering delivers industrial IoT analytics to its own clients: dashboards and reporting built on live telemetry from industrial systems. As demand for that work grew, the in-house team hit a staffing gap it could not close at the right pace. The firm needed senior data engineering capacity, a tech lead to carry technical direction, and people who already understood what SQL Server does under pressure when the data behind it comes from industrial sources.
The brief was not a project build. Greyhound wanted its existing team extended with the missing seniority, client work in flight delivered on schedule, and its own engineers stronger at the discipline by the end than they were at the start.
What we built
Shipverra embedded two people inside Greyhound's analytics team through a dedicated team engagement: a senior data engineer carrying the analytics work and a tech lead owning architecture and code review.
The core of the work was the industrial data analytics pipeline behind Greyhound's client dashboards. Grafana handles visualization, SQL Server is the primary data store, and the N3uron API supplies the industrial telemetry. The hard problem sat in the middle: making Grafana perform well against SQL Server's limitations when the data volumes and update cadences are dictated by industrial systems rather than by the database.
The engagement was also built so capability stayed inside Greyhound rather than walking out with the consultants. Knowledge transfer happened inside the daily workflow, not as a separate training phase: code reviews, architecture discussions, and shared design decisions with Greyhound's own engineers.
Tech stack: Grafana, SQL Server, N3uron, Microsoft Azure, SharePoint.
Outcome
-
Senior data engineering and tech-lead capacity embedded with the in-house team
-
Industrial-analytics dashboard work delivered against client engagements in flight
Greyhound now offers a broader and faster industrial analytics service than it did before the engagement, with senior data engineering expertise embedded in the team instead of bought ad hoc for each project. Client dashboard work that was already in flight kept shipping while the capability was being built.
For an established consultancy, the dedicated team shape solved the underlying math: scalable senior capacity without the overhead and lead time of permanent hires, and an in-house team that keeps the discipline after the engagement.
Engagement line
Engagement FAQ
Can Grafana handle industrial IoT analytics on top of SQL Server?
Yes, but the pairing needs deliberate engineering. In this engagement the central technical problem was making Grafana behave well against SQL Server's limitations when data volumes and update cadences came from industrial systems. That work was carried by a senior data engineer and a tech lead embedded with the client's team.
What data sources feed the industrial analytics dashboards?
The dashboards visualize industrial telemetry pulled through the N3uron API, stored in SQL Server, and rendered in Grafana. The wider environment runs on Microsoft Azure with SharePoint in the workflow.
What does analytics staff augmentation look like in practice?
In this case, two Shipverra roles embedded directly into Greyhound Engineering's existing analytics team: a senior data engineer doing the hands-on work and a tech lead carrying architecture and code review. They delivered against Greyhound's client engagements that were already in flight, inside Greyhound's own workflow.
How does an embedded data engineering consultant transfer knowledge to the in-house team?
Inside the daily workflow rather than in a separate training phase. Code reviews, architecture discussions, and shared design decisions were run with Greyhound's engineers throughout, so the capability stayed in-house after the work shipped.
How fast can an embedded data engineering team start delivering?
This engagement was structured to deliver against client work already in flight rather than starting with a ramp-up project. The two embedded roles picked up live dashboard work for Greyhound's industrial clients from inside the existing team.