Hevo Data
From Hevo Data's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Your Role You will lead a team of 4 to 6 engineers while continuing to be deeply technical.
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Today, supporting that source means an engineer has to understand the API, implement authentication, pagination, schema handling and incremental syncs, test the connector, deploy it and maintain it indefinitely.
We want an agent to do most of this autonomously.
It needs to understand API documentation, generate connector logic, validate the data and deploy the connector.
Then the harder part starts.
APIs change. Schemas evolve. Authentication breaks. Rate limits change. Fields disappear. New failure modes appear in production.
The system needs to detect those changes, understand what happened, decide whether it can safely repair the connector, validate the fix and know when to ask a human for help.
“The AI built it” is not good enough. The connector has to be correct.
If a connector silently produces bad data, a customer dashboard may be wrong and someone may make a bad decision based on it.
Roughly 60 percent of your time will remain hands on. That includes architecture, design, coding, debugging, evaluations, code reviews and production issues.
This is not a people management role with occasional coding.
You will own a major part of the Connector Builder Agent, along with parts of our existing connector platform and database connector infrastructure.
Other teams will own other parts of the system. You will work closely with them.
You will work on problems such as understanding API documentation automatically, inferring authentication and pagination, discovering schemas, handling schema evolution, generating connector code, detecting API changes, repairing failures, scoring confidence and building evaluation systems that tell us whether an autonomous action was actually correct.
You will also help answer harder questions.
How do we know a generated connector is correct?
Which failures can an agent repair safely?
When should the system stop and ask a human?
How do we catch an API change before the customer does?
How do we ensure that a model upgrade does not silently degrade hundreds of connectors?
How do we test systems whose outputs are not deterministic?
You should establish measurable correctness criteria, build strong automated evaluations and define clear confidence thresholds and escalation paths.
Within six months, we expect your part of the system to autonomously build or maintain a meaningful class of connectors with measurable reliability.
You will help them become significantly better engineers.
A year after joining your team, people should be solving harder problems than when they started. Some should be ready for promotion.
If growing engineers does not interest you, this is probably not the right leadership role.
You should already be the person engineers come to when the problem is difficult, even if you have never had a formal management title.
When something breaks in production, you want to understand why.
You have also built real systems with LLMs and have experienced the gap between something working in a demo and surviving production.
You have dealt with outputs that change between runs.
You have built evaluations around probabilistic systems.
You have had to decide when a model should act autonomously and when deterministic software or a human should take over.
You want to lead engineers without giving up engineering.
If your experience with LLMs is primarily prompt engineering or prototypes, the jump will be significant.
If you strongly prefer systems where correctness can be completely captured through deterministic code and unit tests, this problem may be frustrating.
We pay competitively, but compensation alone is unlikely to be the reason someone chooses this role.
You have at least 1 year of technical leadership experience, with or without the formal title.
You have operated production systems and dealt with incidents, on call, reliability and SLAs.
You have built and operated an LLM powered system in production where correctness, reliability, latency or cost mattered.
You have hands on experience with areas such as agents, evaluations, retrieval, tool use, model failure modes or reliability.
You have experience with multi tenant systems, high availability or large data volumes.
Our stack is primarily Java and Kubernetes. If you are strong in another JVM language or a similar systems language, you should be able to ramp quickly.
Experience with data infrastructure, CDC, ETL, database internals or open source systems is useful but not required.
In your second month, form your own view of what trusted means for your area and begin building the evaluations and reliability mechanisms needed to prove it.
This is not a chatbot. It is infrastructure that has to remain correct, quietly, for months.
If that kind of problem excites you, there are not many places working on it this early.
You can talk to anyone in the company, including the CEO.
Architecture decisions in your area are yours.
What you are trusted with depends on what you can do, not your title.
Several of our engineering managers started as senior engineers.