Software Engineer II (JVM)

Nexla is the data layer for enterprise AI. We give AI apps and agents the connectivity, context, and governance to work across more than 1,000 enterprise systems in real time, and we process over a trillion records a month doing it. DoorDash, LinkedIn, Johnson & Johnson, Instacart, and LiveRamp run mission-critical data on us. Honorable Mention in the Gartner Magic Quadrant™ for Data Integration Tools, and top-rated by customers on Gartner Peer Insights four years running. Founded 2016, remote-first, headquartered in San Mateo.

Our values are short and we actually use them: Have Empathy. Be Curious. Be Intellectually Honest. Achieve Excellence. Remember to Relax.

• 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.
• Design decisions inside your scope, and a real voice in the ones above it. Multi-tenancy, concurrency, and latency show up in almost everything here, and you would be expected to have views on the tradeoffs rather than only implementing someone else’s.
• 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.

Roughly 70% building and shipping, 15% design and technical discussion, 15% code review and the RFCs, runbooks, and docs that come with it.

• 5 to 8 years of software engineering, with some of it on backend platforms, services, or SDKs rather than only end-user applications.
• Strong Java. You understand JVM memory management, concurrency models, and non-blocking I/O, and you can work your way to the cause of a thread starvation issue from a heap dump.
• Hands-on experience with distributed systems in production: Kafka, Spark, Flink, or similar. You have run them, not only built against 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.

• 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 cloud-native experience, including Strimzi for Kafka.
• Security architecture: OAuth 2.0, mTLS, and encrypted credential management at scale.

• You want ownership and will take it. You write code every day and you carry what you ship, through deploy and on-call.
• You are comfortable defining scope on the fly and shipping before the full picture is clear.
• You would rather build the guardrail than fix the same failure twice.
• You raise the quality of what is around you through review and pairing, not just the quality of what you write yourself.

One thing to be clear about: our product definition is still moving as AI reshapes data integration, and the platform has to move with it. If you want a stable, well-defined surface to work on, this is not it.

A few large companies - DoorDash, SentinelOne, Johnson & Johnson, LinkedIn, Amex, Integrity Marketing, among them use Nexla for data integration. Connectors, runtime, transformations, scheduling, the parts of the stack where data has to move between systems reliably.

The reason this is an interesting moment to join is what the agent shift is doing to the category. Data integration used to mean "land this data in that warehouse so a human can look at it." That product is mature. What it's becoming is closer to "an agent asks a business question, and the platform figures out which data and what code and which APIs add up to a real answer." That's a much bigger problem, and most of the architecture for it hasn't been built yet by anyone.

A concrete example. A revenue team wants to ask "which enterprise customers are showing renewal risk" and get a real answer through whatever agent or app they work in. Today there is no clean way to answer that question, it requires CRM, usage, support, and billing data joined in org-specific ways, and the calling agent can't just invoke a tool that returns the right answer unless someone first establishes whether the underlying data is even capable of producing one.

That is the system we are building. A probe agent investigates the data plane whether the right fields are populated, whether freshness is adequate, whether the joins exist, whether credentials cover the required scope and returns a grounded feasibility answer. Where there are gaps, Express.dev composes the pipelines to close them. The capability is then exposed as an MCP server: "renewal risk" as a curated product, with the business logic correct and the query semantics described well enough that the calling agent uses it as intended. The MCP Gateway governs which agents can call which capabilities, with the policies, audit, and observability an enterprise control plane requires.

The bet is that ten years of connector work, an enterprise customer base, and a runtime that already moves real volume are the right foundation to define this layer from.