Head of Engineering

Eneba

17h

About The Position

Eneba is seeking a Head of Engineering to own the engineering organization day to day. The role leads technical decisions, organizational structure, process evolution, and AI adoption across more than 70 engineers in Software Engineering, Mobile, Security, and IT Admin. The Head of Engineering reports to the VP of Technology and partners closely with the Director of Product. The role is responsible for the stability, reliability, and efficiency of Eneba’s core technology while supporting iterative platform growth. It involves leading engineering managers and tech leads, developing accountable teams with strong technical ownership, and improving engineering practices, processes, and documentation. The Head of Engineering works with product, data, and other stakeholders to turn business needs into technical priorities, favoring long-term maintainability and incremental improvement. The Head of Engineering anticipates scaling and stability risks, maintains high standards for code quality, security, and operational excellence, and keeps the organization lean. The role also encourages safe experimentation with practices and technologies and establishes the culture, tooling, and behaviors for AI and LLM use across the engineering organization.

Requirements

At least 8 years of engineering experience, including at least 2 years as a Head of Engineering, VP of Engineering, or equivalent senior engineering leader.
Proven experience leading organizations of 60 or more engineers in product-focused technology companies.
Experience managing managers, with engineering managers and tech leads reporting across multiple disciplines such as backend, frontend, and mobile.
Strong hands-on software engineering background, with the ability to make architecture and system design decisions across the stack and earn senior engineers’ trust.
Experience scaling engineering teams in a startup or high-growth environment, making decisions quickly, and operating amid ambiguity.
Experience at consumer-facing product companies, such as marketplaces, e-commerce, gaming, or consumer fintech.
Demonstrated people leadership at scale, including building and coaching engineering managers and tech leads, developing talent, and handling underperformance, restructures, and difficult conversations.
Active use of AI/LLM tools and demonstrated experience driving adoption in an engineering team, including a clear perspective on cost, quality, and use-case trade-offs.
Demonstrated ability to run stable, scalable platforms with a focus on reliability, predictability, and incremental improvement.
Strong stakeholder management and communication skills, including close partnership with product leadership and representation of engineering in executive and cross-functional discussions.
Comfort operating without rigid long-term roadmaps and ability to provide judgment, structure, and continuity while preparing for likely challenges.
Based in Lithuania or willing to travel regularly to the Kaunas headquarters, particularly during the first three months.
Fluent English; business proficiency is required.

Nice To Haves

Experience working in multi-product or multi-project organizations.

Responsibilities

Own the stability, reliability, and efficiency of Eneba’s core technology and ensure the platform runs smoothly as it evolves.
Lead, mentor, and support engineering managers and tech leads, fostering accountability, knowledge sharing, and technical ownership.
Continuously improve engineering practices, processes, and documentation to support onboarding and movement between projects.
Partner with product, data, and other stakeholders to translate business needs into clear technical priorities focused on long-term maintainability.
Anticipate scaling and stability risks and prepare the platform, architecture, and teams to handle them with minimal disruption.
Maintain high standards for code quality, security, and operational excellence while keeping the organization lean and efficient.
Create an environment for safe experimentation with practices and technologies without compromising platform stability.
Set the culture, tooling, and behavior modeling for AI and LLM usage across the engineering organization.