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Hevo Data is a Series B data integration platform (backed by Sequoia Capital, $43M raised) that moves data from 150+ sources into modern data warehouses and lakes for 2,000+ enterprise customers. You will lead an AI-native engineering team building the AI connector framework—a system that generates, tests, and self-repairs connectors when source APIs change.
This is a Technical Lead Manager role, explicitly positioned as the step before Engineering Manager. The company is transparent: in 12–18 months you'll either grow into running a larger team as an EM or pivot to Staff/Principal IC work. Either path is open.
Your core responsibilities:
- Lead a team (starting ~5 engineers, growing with the company) building platform services and database connectors that power mission-critical data pipelines
- Own the AI connector framework architecture: how it generates connectors, evaluates their quality, and handles self-repair when source systems change
- Embed AI into every workflow—design, code review, testing, production debugging—and teach your team to work this way
- Set and enforce standards for observability, reliability, security, and auditability across all shipped systems
- Solve hard distributed systems problems: exactly-once delivery under component failures, multi-tenancy isolation, mid-flight schema changes, and high-volume data handling (100B+ records/month, petabytes/month)
- Make architectural decisions in your area, define the framework's scope, decide hiring bar, and have direct input on engineering strategy
- Retain ~60% technical work: you won't be removed from code over time
What makes this interesting: Hevo is building at real scale (2,000+ customers, 150+ source systems) and embedding AI into the engineering workflow itself—not as a side tool but as core to how connectors are generated and debugged. Few engineers in India are currently paid to work this way. You'll learn how to lead teams where AI is embedded in the work, a skill that will be table-stakes for engineering leaders in 2–3 years.
First-year success looks like: the AI framework generating production connectors with quality bars you designed; new connector shipping time dramatically reduced and measurable; you've hired 2–3 engineers who are shipping; at least one team member promoted.
The company is growing fast (engineering up 30% in 4–5 months), so your scope expands with it. You'll have direct access to engineering leadership—no layers between you and decision-making.
REQUIREMENTS:
- 8+ years backend or distributed systems engineering
- At least 1 year leading a team (formal or informal; if you're already the person everyone goes to, that counts)
- Production on-call experience: incidents, SLAs, and you've been there when systems broke
- Strong Java or another JVM language; Kafka and Kubernetes in production
- Real experience with multi-tenant systems, high availability, or data at volume
- You've built something with LLMs in production (agents, evaluations, retrieval, prompt systems, etc.)—not just used a coding assistant
- Ability to explain complex systems to non-engineers
- Experience giving hard feedback and maintaining relationships afterwards
