Engineering Manager

Nexla, briefly

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.

Role, brieflyTwo 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.

What you’ll own

• 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.