Databricks Didn’t Just Buy Neon for the Tech — They Bought the Talent
When Databricks announced its acquisition of Neon today, most headlines zoomed in on the obvious: serverless Postgres, a Rust-based decoupled storage engine, and ephemeral database spin-ups tailor-made for AI agents.
But let’s be clear.
This deal wasn’t just about database tech. It was a $600M talent acquisition — a calculated acqui-hire of one of the most elite, performance-obsessed engineering teams in the cloud-native Postgres ecosystem.
Neon isn’t just a cool open-source repo with a nice logo and a slick developer experience. Behind the marketing veneer is a core engineering team of 88 people — not generalists, not full-stack dabblers — but infrastructure purists. Engineers who live in the guts of query execution, write I/O schedulers in Rust for fun, and casually debate the physics of memory layout over Slack.
- Many are former Postgres core contributors.
- Others cut their teeth building distributed systems at AWS, Google, Cloudflare, and Red Hat.
- And more than a few have OSS projects with tens of thousands of GitHub stars under their belt.
This is the kind of team that doesn’t just ship features — they challenge the fundamental assumptions of what a database should be in an AI-native world.
Databricks knew exactly what they were buying.
They weren’t just acquiring a company. They were absorbing a capability — one they couldn’t afford to compete with, especially as the arms race for AI-native infrastructure accelerates.
As agentic systems proliferate, traditional databases — which assume humans are the bottleneck — are falling apart. Neon’s architecture flips that on its head, designing from the ground up for ephemeral, compute-bursty, high-concurrency environments where AI agents, not humans, are the primary users. It’s no surprise that four of Neon’s engineers made the 2025 LeadGenius AI 1000, our independently curated list of the most consequential technical builders in AI and data infra.
This isn’t a bet on market share. It’s a bet on the builders.
Because in the era of AI-native workloads, infrastructure isn’t something you rent — it’s something you craft, control, and continuously optimize. And Databricks just bought themselves one of the best craft teams in the game.
Let’s meet some of them.
🔧 Roman Zaynetdinov — The Postgres Polyglot
- Title: Systems Engineer, Neon
- Years of Experience: 10
- Languages of Choice: Rust, Go, TypeScript
- Core Skills: Edge computing, encryption, Postgres internals, serverless infra
- Fun Fact: Built local-first database architectures on top of LiteFS and Fly.io before joining Neon.
Roman Zaynetdinov is the kind of engineer most teams dream about and few ever find — a rare deep-stack polymath who can architect distributed databases one moment, tune write-ahead logs the next, and still find time to prototype an interactive frontend visualization in D3.js before lunch.
His range isn’t just wide — it’s shockingly precise. Roman moves fluidly between systems-level code in Rust, performance-critical Postgres internals, and developer-experience layers that bring elegance to complexity. That kind of vertical versatility is what made him indispensable at Neon.
Roman was a key contributor to Neon’s architectural moonshot: decoupling compute and storage in a way that didn’t just preserve query speed — it improved it. While others were chasing theoretical abstractions of serverless databases, Roman was building real-world mechanisms that made ephemeral workloads feel persistent and stateful. He helped engineer Neon’s ability to spin up entire Postgres instances in milliseconds, without sacrificing indexing fidelity or concurrency integrity.
His superpower? He understands the full feedback loop — from byte-level optimization to developer usability. That makes him not only a technical asset, but also a critical thought partner in product shaping, system observability, and real-time performance debugging.
In a post-human-query world — where AI agents orchestrate data systems in real-time — Roman’s skillset becomes even more essential. At Databricks, he’s positioned to help architect the next evolution of ephemeral, just-in-time data compute layers that can support millions of concurrent agents with near-zero latency.
🧠 Elena Grahovac — The Orchestrator of Chaos
- Title: Engineering Manager, Neon
- Years of Experience: 10
- Core Skills: Kubernetes, observability (Datadog, Grafana), microservices architecture, stakeholder wrangling
- Toolbox: Go, Python, R, RabbitMQ, Vault, Spinnaker
Elena doesn’t just build systems — she transforms entropy into high-output engineering velocity. With a rare blend of technical depth and leadership fluency, she’s spent the last decade scaling distributed systems while scaling the teams that maintain them — a dual skill set few engineers ever master.
At Neon, Elena served as the connective tissue between the platform’s bleeding-edge architecture and the business imperative to ship reliably, quickly, and securely. She led engineering efforts across Kubernetes orchestration, observability tooling, and CI/CD automation, ensuring that a complex microservices ecosystem remained nimble even as Neon’s customer base and deployment footprint exploded.
But her impact isn’t just code-level — it’s cultural.
Elena has been a force multiplier for team cohesion, process maturity, and agile transformation. She’s the kind of leader who can map distributed tracing workflows one day, and coach junior engineers on incident response frameworks the next. Her approach to platform engineering is both empathetic and unflinching: minimize toil, maximize trust, and ruthlessly optimize for dev productivity.
With Databricks now inheriting Neon's core stack, Elena’s role becomes even more pivotal. She’s not just a manager — she’s a systems integrator in the broadest sense: aligning people, process, and platform in a way that unlocks scale without sacrificing speed.
In the race to build AI-native infrastructure, she’s exactly the kind of leader you want upstream of every major deployment decision.
🔐 Krzysztof Szafrański — The Full-Stack Security Sage
- Title: Security Engineer, Neon
- Years of Experience: 10
- Strengths: CI/CD, application security, GIS + geospatial data, PostGIS, OAuth
- Secret Weapon: Fluent in 4 languages and probably writes in 6 programming ones
Krzysztof isn’t just a security engineer — he’s a full-stack threat anticipator, someone who embeds security at the architectural level rather than bolting it on as an afterthought. In a world where AI-driven agents generate code, trigger workflows, and access sensitive data in milliseconds, Krzysztof’s work at Neon has been mission-critical to keeping their platform not just functional, but defensible.
He’s one of the rare security leaders who moves at the speed of DevOps — automating policy enforcement, streamlining vulnerability detection, and integrating penetration testing directly into CI/CD pipelines. At Neon, he architected API-first security protocols that allowed developers to move quickly without compromising compliance, auditability, or attack surface visibility.
His secret weapon? Context-aware security.
Krzysztof doesn’t just patch. He predicts. Whether it’s designing secure OAuth workflows for agent-driven data access, optimizing PostGIS configurations to limit geospatial data exposure, or hardening Rust-based microservices against supply chain exploits, his contributions extend far beyond the conventional security checklist.
As the lines blur between data infra and AI orchestration, Krzysztof’s experience building secure-by-default systems will be essential. Expect him to shape how Databricks rethinks trust boundaries in a world where agents make API calls, spin up databases, and chain actions autonomously.
In short: He’s not just securing endpoints. He’s securing the future of AI-native compute.
🧪 Alexey Kondratov — The Physicist-Turned-Postgres-Performance-Ninja
- Title: Software Engineer, Neon
- Years of Experience: 11
- Past Life: Computational physicist working on electromagnetic simulations
- Languages: C, Ruby on Rails, Python
- Specialties: Simulation architecture, theoretical modeling, Postgres tuning
Alexey doesn’t just write performant code — he builds systems the way physicists build models: with mathematical precision, deterministic predictability, and an obsession with first principles.
With a background in computational physics and electromagnetic simulations using finite-difference time-domain (FDTD) methods, Alexey brings a unique analytical mindset to systems engineering. He approaches every architectural decision with scientific rigor — modeling latency, throughput, and concurrency not as rough guesses, but as solvable equations.
At Neon, Alexey was instrumental in architecting the predictive query execution engine — a system optimized not just for speed, but for consistency across highly variable workloads. His work helped Neon balance ephemeral spin-up times with persistent performance, allowing the platform to support thousands of concurrent AI agents querying in real-time without degradation.
He thinks about infrastructure the way a physicist thinks about energy: nothing should be wasted, every transaction has a cost, and throughput can always be optimized with the right structure.
But his value doesn’t stop at performance engineering.
Alexey has also been a thought partner in Neon’s broader product strategy — helping translate advanced systems architecture into developer-friendly abstractions, and ensuring the open-source stack scales cleanly with demand.
As Databricks integrates Neon's architecture into its broader Data Intelligence Platform, Alexey’s brand of systems rigor will become a strategic differentiator. In an AI-native future where latency isn't just a metric but a constraint on model orchestration, his ability to build ultra-efficient, low-jitter systems will be a foundational advantage.
Simply put: Alexey doesn’t just make Postgres faster — he makes AI infrastructure physics-grade.
Talent is the New Infrastructure
Databricks isn’t just acquiring an open-source Postgres engine. They’re acquiring the developers behind it — the people who understand not just how to build modern data systems, but how to rethink them entirely for a world of autonomous agents and stateless compute.
And in today’s AI-driven stack, that matters more than ever.
These 88 engineers? They don’t just know how to write code. They know how to challenge assumptions at the database layer, fight for performance per watt, and move fast without breaking trust.
As Ali Ghodsi put it:
“Four out of five databases on Neon are spun up by agents — not humans. That’s the future. And this team built for it.”
So yes, Databricks bought Neon for the product.
But make no mistake.
They paid a premium for the people.
