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Nexla is a data integration platform built with AI for AI, enabling enterprises to connect over 1,000 SaaS applications, databases, APIs, and data streams in real time. The company processes over a trillion records monthly and is trusted by DoorDash, LinkedIn, Johnson & Johnson, Instacart, and LiveRamp. Named in the Gartner Magic Quadrant for Data Integration Tools four years running.
You will lead Nexla's connector layer—the critical infrastructure that abstracts and communicates with thousands of external systems, each with unique schema quirks, authentication schemes, rate limits, and retry semantics. This is a hands-on technical leadership role where you'll be the most senior engineer on the team, with four to six engineers reporting to you. The split is approximately 80% hands-on design and code, 20% team growth and management.
Key responsibilities:
- Own the architecture of the connector layer, making design decisions on how to abstract a thousand systems and handle formats like JSON, Parquet, and Avro at high throughput. You'll write the hardest parts yourself.
- Drive the connector roadmap: decide what ships, what gets deprecated, and prioritization, working directly with product, customer success, and support teams.
- Ensure reliability of shipped systems, including on-call responsibilities. Build tooling so the team detects problems before customers do.
- Hire, mentor, and grow four to six engineers, raising the technical bar primarily through design review and pairing. Formal management is roughly one day per week.
The core challenge: thousands of connectors power thousands of customer pipelines, each requiring scale in two dimensions—data volume and reliability under load. This is a standing, evolving problem as the connector surface continuously grows.
Required qualifications:
- 10+ years building backend systems; you remain hands-on and can point to code you wrote recently.
- Deep JVM expertise, including debugging distributed systems where failures span multiple components.
- Production experience with Kafka, Redis or Memcached, APIs over gRPC and REST, and Kubernetes at high throughput.
- Technical leadership experience: design review, mentoring, raising team standards. Formal people-management experience is a plus but not required.
- Active use of AI tools in your work with specific understanding of where they help and mislead.
Bonus: data integration, ETL, or iPaaS background; experience wrangling JSON, Parquet, Avro at scale; direct report experience including performance conversations.
You'll thrive if you default to urgency (ship a good answer this week, not a perfect one next quarter), own outcomes rather than just your slice, and close loops without routing problems between teams.
