Built by engineers who spent too long wiring pipelines
Nava Labs was founded in San Francisco in 2023 with a single premise: the data engineering bottleneck is structural, not technical. We built the layer that removes it.
The problem we kept running into
Before starting Nava Labs, our founding team spent years at data-heavy companies in fintech, e-commerce, and developer tooling. We ran data engineering orgs, built pipelines, and watched the same pattern repeat: every new data source triggered a multi-week project. Every question that crossed two systems meant a new ETL job.
The tooling had improved dramatically for individual systems. Warehouses got faster. Query engines got smarter. But the connective tissue between systems still required manual engineering for every combination.
We started with a simple question: what if connecting a data source was as easy as adding credentials, and querying across sources used the SQL you already know? That question became Nava Labs.
We closed our angel round in late 2025 and have been building toward early access with a cohort of data engineering teams who share the same frustrations we had.
The people building Nava Labs
Vyas Krishnan
Vyas spent years leading data infrastructure at large B2B SaaS platforms, scaling pipelines across dozens of sources. Vyas co-founded Nava Labs to remove the onboarding friction that kept slowing teams down.
Alex Novak
Alex spent years working on query federation and cross-cluster coordination at a distributed-systems infrastructure company before Nava Labs. Alex designed the Nava Labs query engine from the ground up.
Maya Chen
Maya built connector ecosystems at two data observability startups before joining Nava Labs. Maya leads connector development and schema drift detection, drawing on years of debugging upstream API failures across enterprise sources.
Try Nava Labs during early access
We are accepting a limited number of engineering teams. Free tier available immediately, no credit card required.