Senior Engineering Manager

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, briefly Two systems sit at the centre of Nexla, and this role covers both. The connector layer talks to everything else: over a thousand SaaS applications, databases, streams, files, and APIs, bidirectionally, each with its own schema quirks, auth scheme, rate limits, and retry semantics. The runtime is what moves the data once a connector has it, built on Kafka, our own execution engine, and Ray for AI workloads, at billions of events a day with sub-second latency. You would own the engineering across both, along with the DevOps and QA work that keeps them shippable and running. The split today is roughly half design and code, half leading the team. You are expected to be in the architecture, in the reviews, and in the hard debugging sessions, not managing from a distance.

• The architecture across connectors and runtime. How we abstract a thousand systems, how we handle JSON, Parquet, and Avro at throughput, how work gets scheduled and recovered when a node dies. You make those calls and you write the hard parts yourself.

• Reliability of everything your team ships, on-call included. Fewer failures reaching customers, faster detection when they do, and a release and test path that catches the obvious things before a customer does.

• What ships and in what order. You work directly with product, customer success, and support on what customers actually need, and you decide what gets built, what gets deprecated, and what waits.

• The engineers around you. Hiring, growth, and raising the technical bar, mostly through design review and pairing rather than process. As the team grows you will build tech leads under you and hand off implementation ownership, without handing off architectural accountability.

Ninety days in: you have shipped something non-trivial to production, named the two worst parts of the current design with a proposal attached, and the team has started routing hard design questions to you before they route them upward.

A real problem you’d hit here We have thousands of connectors with thousands of customer pipelines running on them, and a runtime underneath that has to hold throughput and reliability at the same time. Neither property is something you fix once. The connector surface keeps growing, every new system brings its own failure mode, and the interesting bugs almost always live at the seam between a connector and the runtime, where neither team's instrumentation quite reaches. Closing that gap is the standing problem of this team.

Must-haves

• 12+ years building backend systems, and you are still hands-on. You can point at code you wrote recently and are proud of.

• Deep JVM experience. You have debugged distributed systems where the failure was not in the code you wrote.

• Production depth in Kafka, Kubernetes at high throughput, Redis or Memcached, and APIs over gRPC and REST.

• You have led engineers technically and carried direct