Lead an AI-native engineering team building the next-generation connector framework for enterprise data pipelines at Hevo Data.

About the Company

Hevo Data is a leading data integration platform that powers mission-critical data pipelines for over 2,000 enterprise customers across 40+ countries, including notable names like Pinterest, Gartner, Shopify, Iceland Air, and Jawa Motorcycles. The company moves data from 150+ diverse source systems into modern data warehouses such as Snowflake, BigQuery, and Databricks without requiring customers to write code or dedicate engineering resources. Hevo Data is a Series B company that has raised $43 million from prominent investors including Sequoia Capital, Chiratae, and Qualgro. With dual headquarters in San Francisco and Bangalore, Hevo’s core platform engineering is built in India.

Role Overview

This is not a traditional team lead role—it is a stepping stone to Engineering Manager (EM), with a clear 12-18 month progression path. If you are already the person everyone turns to for design reviews, late-night incident responses, and career advice, this role formalizes that leadership. As a Technical Lead Manager (TLM), you will run an AI-native engineering team building the AI connector framework that generates, tests, and autonomously repairs connectors when source systems change. Your team will own highly scalable, reliable, and secure platform services and database connectors that carry real production weight—when a connector breaks, revenue reporting is impacted that same morning. The scope includes end-to-end responsibility for pipelines into Snowflake, BigQuery, Databricks, and Apache Iceberg, each with unique semantics, failure modes, and performance envelopes.

Key Responsibilities

• Lead the development of the AI connector framework: a system that automatically generates connectors, validates them through evaluation pipelines, and repairs them when source APIs change unexpectedly.
• Architect and deliver platform services and database connectors that handle 100 billion records and petabytes of data monthly across 2,000+ customers.
• Embed observability, reliability, security, and auditability as defaults in every shipped component—not as bolt-ons.
• Solve hard distributed systems problems: exactly-once delivery during component failures, multi-tenancy with strict isolation, and handling schema changes arriving mid-flight from unannounced source changes.
• Hire and grow a team of 5+ engineers (starting with 5, expanding rapidly with company growth).
• Maintain 60% hands-on technical work, including writing specifications, evaluations, guardrails, and application code.
• Teach the team to incorporate AI into every workflow: design, review, testing, and production debugging.
• Make architectural decisions independently, set the hiring bar, and determine where AI is the right tool versus where it is not.

Tech Stack

• Backend: Java (primary), JVM languages, Kafka, Kubernetes
• Data: Snowflake, BigQuery, Databricks, Apache Iceberg
• Infrastructure: Production-grade multi-tenant systems, high availability, data at volume
• AI/ML: LLMs, agents, evaluations, retrieval, prompt systems in production
• Other: Distributed systems, exactly-once delivery, schema evolution, observability tooling

What a Good First Year Looks Like

• The AI framework generates production connectors with a quality bar set by evaluations you designed.
• Shipping a new connector takes a fraction of the current time, provable with metrics.
• You have hired two to three engineers yourself, and they are shipping production code.
• At least one person on your team has been promoted.

Why Join Hevo Data?

• Real decision-making authority: you decide architecture, framework capabilities, and hiring standards.
• Your scope grows with the company—engineering is up 30% in the last four to five months.
• Direct access to engineering leadership—no layers of management between you and decision-making.
• You don’t have to stop coding—60% of your week stays technical.
• Learn to run an AI-native team, a skill that will be expected of every engineering leader in two years.

Hiring Process

We may use AI tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist but do not replace human judgment—final hiring decisions are made by humans.

8+ years of backend or distributed systems experience; at least 1 year of team leadership (formal or informal); strong Java or JVM languages with Kafka and Kubernetes in production; real experience with multi-tenant systems, high availability, or data at volume; built something with LLMs in production (agents, evaluations, retrieval, prompt systems); ability to explain complex systems to non-engineers; experience giving hard feedback and maintaining relationships.

Direct decision-making authority over architecture, hiring, and technical direction; rapid scope growth with company scaling (30% engineering growth in recent months); direct access to engineering leadership; 60% technical work remains hands-on; opportunity to lead an AI-native team ahead of industry curve.