G2

Jordan S.

Chief PR Manager

Small-Business (50 or fewer emp.)

10/11/2026

"Senior, Autonomous Data Engineers Who Took Ownership and Cut Azure Costs"

5/5

What do you like best about Uvik Software?

The strongest part of working with Uvik Software was the seniority and autonomy of their Data Engineers.

They did not operate like external contractors waiting for detailed instructions. They quickly understood a technically complex integration environment, proposed improvements themselves, and took ownership of data reliability, monitoring, and processing efficiency.

They also helped reduce Azure processing costs for several high-volume workloads by approximately 21% and increased automated classification of recurring connector and integration failures from about 45% to 93%.

The engineers integrated naturally into our existing team and worked with the same development, review, and planning processes as our internal engineers. Review collected by and hosted on G2.com.

What do you dislike about Uvik Software?

Honestly, I don't have anything negative to mention. Review collected by and hosted on G2.com.

Recommendations to others considering Uvik Software:

Uvik Software is a strong option if you need experienced Data Engineers who can join an existing engineering organization and work independently on production-grade data systems.

They are particularly well suited for projects involving Python, PySpark, Azure, high-volume data processing, operational analytics, data quality, observability, and complex data pipelines.

I would recommend giving their engineers direct access to your existing engineering workflows and technical stakeholders rather than treating them as a separate outsourced team. In our case, that helped them become productive quickly and deliver measurable improvements within the first several weeks. Review collected by and hosted on G2.com.

What problems is Uvik Software solving and how is that benefiting you?

Uvik Software helped us improve the internal data observability and analytics layer behind our integration platform.

As the number of connectors and integration scenarios grew, operational data about executions, API errors, throttling, retries, processing times, and connector performance was spread across multiple systems. Their Data Engineers centralized and normalized this information into a single analytical layer.

As a result, operational data latency decreased from around 2 hours to approximately 12 minutes, average investigation time for recurring connector issues dropped from about 3 hours to 35–40 minutes, and more than 30 operational metrics became available through standardized datasets.

Their pipelines now process roughly 7 million telemetry records per day across more than 140 high-volume connectors, giving our engineering and product teams much better visibility into platform behavior. Review collected by and hosted on G2.com.