Job Description

Who we are

As the global leader in nature-based smart city solutions, we help cities around the world become greener and more resilient against climate change. We apply the latest remote sensing technologies and develop machine learning algorithms and software to automate the analysis of urban trees and green spaces. With our cloud-based platform, cities can improve the operational efficiency of green space management and strategic city planning to create a greener, healthier, safer, and more resilient urban environment. We currently have offices in Berlin, Paris, San Francisco, and Singapore, with production headquarters in Budapest.

The Engineering Manager is a hands-on technical leader responsible for scaling engineering teams, systems, and delivery capabilities in a fast-growing environment. The role combines active contribution to architecture and critical development with establishing structure, standards, and predictable execution as the organization evolves.

A key focus is transforming engineering into a structured, scalable, and outcome-driven function. This includes introducing clear processes, improving planning and prioritization, and enabling consistent, high-quality delivery across multiple initiatives.

The role operates across the full engineering lifecycle ensuring that solutions are scalable, maintainable, and aligned with business objectives. It also plays a central role in reducing technical complexity, improving system performance, and strengthening overall engineering discipline.

In parallel, the Engineering Manager drives adoption of modern technologies and practices, including cloud-native solutions and AI-enabled development, to increase productivity, accelerate delivery, and improve quality. The role balances hands-on technical involvement with team leadership, fostering ownership, accountability, and continuous improvement as the organization scales.

Key Responsibilities

Hands-on Technical Leadership

• Remain actively involved in architecture, system design, and selected critical implementations

• Guide engineers through complex problem-solving, technical trade-offs, and system evolution

• Drive adoption of modern technologies to improve engineering productivity and quality

• Ensure systems are designed for scalability, multi-tenancy, and long-term maintainability

• Lead code and design reviews, maintaining a consistently high engineering standard

AI-enabled Engineering

• Define and implement a structured approach to AI adoption across engineering workflows. Ensure AI usage is embedded in daily workflows rather than ad-hoc:

• AI-assisted coding, reviews, and refactoring

• Automated test generation and QA acceleration

• Intelligent debugging and root cause analysis

• Measurable (productivity, defect reduction, cycle time)

• Secure and compliant adaption

• Drive consistency in tooling and practices across teams to avoid fragmentation

Delivery & Execution

• Own delivery predictability across multiple parallel initiatives

• Balance delivery speed, quality, and technical debt

• Ensure prioritization is clear and aligned with business objectives

• Actively manage cross-team dependencies and integration points

• Transition teams from reactive execution to structured, planned delivery

Engineering Process & Standardization

• Establish scalable and consistent engineering processes:

  • Sprint planning, backlog management, and delivery tracking

  • Release management with rollback capabilities

  • Incident, problem, and change management discipline

• Introduce practical standards that improve speed and reduce ambiguity without over-engineering

• Reduce reliance on individuals through improved documentation, automation, and knowledge sharing

Team Development & Scaling

• Build and scale a high-performing engineering team

• Coach engineers and develop emerging technical leaders (senior engineers, tech leads)

• Increase delegation and distributed ownership to remove bottlenecks

• Strengthen accountability and outcome-oriented delivery culture

Quality, Reliability & Performance

• Improve system reliability through better engineering practices and architectural discipline

• Ensure a scalable testing strategy across units, integration, regression, and automation layers

• Partner with DevOps/SRE to strengthen observability and operational excellence

• Drive continuous improvement through incident learnings and performance insights

Required Qualifications

• 10+ years of software engineering experience

• 3+ years in engineering leadership roles in growth or scale-up environments

• Strong hands-on experience with:

  • Node.js / Java / Python

  • React / modern frontend frameworks

  • SQL (PostgreSQL) / NoSQL (MongoDB, Redis)

  • Kubernetes, Docker

  • REST APIs / GraphQL

  • Git / GitHub

  • CI/CD pipelines (GitHub Actions / Azure DevOps)

  • AI-assisted development tools (e.g., GitHub Copilot)

  • Microservices / event-driven architecture / API design

  • Monitoring & observability (preferred Grafana)

• Proven experience with cloud platforms (Azure and/or AWS) in production environments

• Experience introducing or scaling AI tools in engineering workflows

• Demonstrated track record of moving teams from ad-hoc to structured, scalable delivery models

Key Competencies

• Ability to scale systems, teams, and processes in parallel

• Embedding and leveraging latest technologies to improve productivity and quality

• Strong technical judgment with pragmatic decision-making

• Comfortable operating in ambiguity while introducing structure

• Focus on outcomes over activity

• Balanced approach between hands-on involvement and effective delegation

• Strong ownership and accountability mindset

Please note, our office attendance is high, with employees working on-site four days a week.