Nexla, briefly
Nexla is the data connectivity layer for enterprise AI. Over 1,000 bidirectional connectors give agents real-time access to data and actions over MCP. Nexla's Agentic Data Integration handles high-volume streaming ETL, CDC, and Document ETL. At the core of the platform is Helix, a context engine that continuously learns from metadata, schemas, telemetry, and documentation to give agents accurate context and keep pipelines reliable. Nexla processes over a trillion records a month for enterprises including DoorDash, LiveRamp, LinkedIn, and J&J. Honorable Mention in the Gartner Magic Quadrant™ for Data Integration Tools, and top-rated by customers on Gartner Peer Insights four years running.
Role
You would work on the backend systems that move data through Nexla: JVM services, the streaming runtime on Kafka, and the distributed processing underneath. Production Java every day. Nobody hands you a finished design. You own systems end to end, from design through deploy to on-call, and you debug them when they break. The role sits between our platform and integration layers, so you would work across both rather than inside one. Small, focused team, which is why the ownership is real rather than nominal.
What you'll own
Core framework features, designed and shipped by you. Schema evolution, pagination, rate limiting, circuit breaking, and the automated retries and self-recovery that keep pipelines running without a human watching. The SDKs and interfaces other teams build on sit here too: you write them, document them, and use them yourself.
The streaming and processing path in production. Kafka, Spark, and Flink workloads, checkpointing, backpressure, and failure recovery. You operate what you build.
Technical direction. Architecture for multi-tenancy, high concurrency, and low latency, plus the technology calls underneath it: where the JVM is the right answer, where an alternative runtime is better, and how to cut infrastructure cost without giving up reliability.
The hard debugging. Trace failures across Kafka clusters, distributed runtimes, and flaky third-party APIs, fix them, then build the guardrails so they do not come back.
Staff scope: you work directly with Product and the CTO to turn customer needs and market shifts, from GenAI data ingestion to real-time CDC and agentic workflows, into technical plans. On a team this size your standards become the team’s standards.
Roughly 60% building and shipping, 25% architecture and technical direction, 15% code review, design sessions, and the RFCs, runbooks, and docs that come with them.
The hard problem
Our platform integrates with more than a thousand external systems, each with its own schema quirks, auth scheme, rate limits, and retry semantics. The framework problem is the N+1 one: making the thousandth integration as cheap to add as the tenth. That means absorbing the differences into the abstraction rather than special-casing them, while the data underneath keeps moving at production volume.
Must-Haves
10 to 14 years of software engineering, with significant time building platforms, frameworks, or SDKs rather than end-user applications.
Expert-level Java. You understand JVM memory management, concurrency models, and non-blocking I/O well enough to diagnose a thread starvation issue from a heap dump.
Deep experience with distributed systems in production: Kafka, Spark, Flink, or similar. You have operated these systems, not just used them.
Comfort with the modern data stack and the protocols around it: Snowflake, Databricks, gRPC, GraphQL, webhooks, and CDC.
You have owned production systems and know what good operational discipline looks like.
You use AI tools in your actual work and can say specifically where they help and where they mislead you.
Bonus points
Contributions to open-source data projects such as Airbyte, Flink, Debezium, or Arrow.
Time at an iPaaS or ELT company: MuleSoft, Fivetran, Informatica, Airbyte, or similar.
Deep Kubernetes and…