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Nexla is the data layer for enterprise AI, enabling AI apps and agents to work across 1,000+ enterprise systems in real time while processing over a trillion records monthly. Customers include DoorDash, LinkedIn, Johnson & Johnson, Instacart, and LiveRamp. The company is remote-first, founded in 2016, and headquartered in San Mateo.
In this role, you will work on backend systems that move data through Nexla: JVM services, the streaming runtime on Kafka, and the distributed processing layer underneath. You'll write production Java daily, receiving well-scoped problems that you own end-to-end—from design through production deployment. Working in a small team means your code reaches customers quickly and you see its real-world impact.
Key responsibilities include:
- Building features end-to-end: shaping the approach with your lead, implementing, testing, shipping, and supporting in production
- Owning code quality through comprehensive testing and readable, maintainable code
- Debugging production issues, tracing failures through Kafka, distributed runtimes, and third-party APIs
- Building domain fluency in data integration challenges: schema variations, authentication schemes, rate limits, and retry semantics
- Spending roughly 80% building and shipping, 20% on code review, design discussions, and learning surrounding systems
The core technical challenge: Nexla integrates with 1,000+ external systems, each with unique quirks. The platform must make the thousandth integration as cheap to add as the tenth by absorbing differences into abstractions rather than special-casing them, all while maintaining production data volume. You won't own this problem alone but will work on pieces of it from the start.
The role sits closest to the integration layer where the platform meets external systems. You'll thrive if you finish things, ask for help early after trying solutions, read unfamiliar code without complaint, and want hard feedback to improve quickly.
The company is actively reshaping data integration around AI. Product definition is still evolving, so expect a dynamic, moving surface rather than a stable one.
Hiring process: Four conversations over roughly two weeks covering coding (real code, not puzzles), low-level design (class/small system structure), high-level design (larger systems calibrated to experience), and a hiring manager conversation on learning, ownership, and AI tool usage. You're encouraged to use AI tools in the coding round as you would on the job; the company evaluates your judgment in delegation, verification, and filtering.
Location: Bengaluru, hybrid, 2 days per week in office. You'll need to overlap with morning Pacific hours for syncs, design reviews, and collaboration with US-based leadership and engineering teams. Compensation includes base salary and equity, set by depth and experience.
**Requirements:**
- 2 to 5 years of professional software engineering with Java as primary language
- Solid JVM fundamentals: collections, concurrency basics, memory behavior, ability to read stack traces and thread dumps
- Write tests as part of building, not after request
- Comfortable with SQL and at least one of: gRPC, REST, webhooks, or CDC
- Active use of AI tools in actual work with specific understanding of where they help and mislead
**Bonus:**
- Production exposure to Kafka, Spark, Flink, or similar distributed systems
- Kubernetes or experience debugging non-laptop environments
- Open-source contributions, side projects, or built work you can walk through
- Data integration, ETL, or experience cleaning messy data
