- Страна
- Ирландия
Откликайтесь
на вакансии с ИИ

Software Engineer
Kargo — стабильная компания с 20-летней историей и множеством наград, предлагающая работу над современным стеком (AI, Big Data). Высокий балл за интересные задачи и сильную инженерную культуру, однако требование присутствия в офисе 4 дня в неделю может подойти не всем.
Сложность вакансии
Роль требует более 4 лет опыта работы с высоконагруженными бэкенд-системами и глубокого понимания архитектуры данных. Кандидат должен быть готов к гибридному формату работы (4 дня в офисе) и демонстрировать навыки 'AI-native' инженера.
Анализ зарплаты
Зарплата для Software Engineer в Уотерфорде с опытом 4+ года обычно находится в диапазоне 60,000–80,000 евро в год. Учитывая международный статус Kargo и сложность задач, можно ожидать предложение по верхней границе рынка или выше. Крупные тех-хабы вроде Дублина платят больше, но для регионального центра это конкурентная позиция.
Сопроводительное письмо
I am writing to express my interest in the Software Engineer position at Kargo. With over 4 years of experience in building robust backend services and a strong focus on high-volume data ingestion, I am confident in my ability to contribute to the CDA team's mission of scaling dynamic, catalogue-driven advertising. My background in designing clean API boundaries and managing distributed systems aligns perfectly with your goal of maintaining a high-performing data infrastructure for global retailers.
In my previous roles, I have consistently demonstrated ownership by taking features from technical design through to production monitoring. I am particularly excited about Kargo's 'AI-native' approach, as I have actively integrated AI-assisted development tools into my workflow to enhance code quality and delivery speed. I look forward to the opportunity to bring my technical expertise and collaborative mindset to your Waterford team and help elevate the platform's reliability and performance.
Составьте идеальное письмо к вакансии с ИИ-агентом

Откликнитесь в kargo22 уже сейчас
Присоединяйтесь к Kargo, чтобы создавать будущее рекламных технологий на основе ИИ и данных мирового масштаба!
Описание вакансии
Who We Are
Kargo creates powerful moments of connection between brands and consumers to build businesses. Every day, our 600+ employees work to radically raise the bar on what agentic AI, CTV, eCommerce, social, and mobile can do to deliver unique ad experiences across the world’s most premium platforms. Taking a creative science approach to all we do, we continuously innovate solutions that outperform industry benchmarks and client expectations. Now 20+ years strong, Kargo has offices in NYC, Chicago, LA, Dallas, Sydney, Auckland, London and Waterford, Ireland.
Who We Hire
Techies who want to build the future. Creatives who want to design it better. Communicators to win business. Collaborators to build it. Data pros who turn numbers into insights. Product builders who turn ideas into innovations. Anyone eager to be on a team that doesn’t stop to ask what’s next, because they’re already building it.
Mission
This role exists to build and maintain the data infrastructure that powers dynamic, catalogue-driven advertising at scale. The CDA team owns the feeds and data ingestion layer — the systems that transform large-scale product catalogues and transaction data from retailers into the building blocks of high-performing ads across Facebook, Instagram, Snapchat, Pinterest, and beyond. Without this engineer, the platform doesn't scale, the data doesn't flow, and advertisers can't deliver.
*This is a hybrid role requiring onsite presence 4 days per week.*
Outcomes — What Success Looks Like in 6–12 Months
- Owns features end-to-end: Has shipped multiple features independently — from technical design through deployment and monitoring — with minimal oversight and strong production quality.
- Meaningfully improves data ingestion reliability: Identifies and resolves bottlenecks or failure points in the feeds/ingestion pipeline, measurably improving uptime, latency, or data accuracy for downstream teams.
- Earns trust as a platform thinker: Demonstrates clear API boundary design and system decisions that balance the needs of consuming teams with long-term platform health — recognized by peers and leads.
- Elevates team code quality: Consistently delivers thorough code reviews that catch real issues, improve maintainability, and raise the bar on testing and observability practices across the team.
- Grows as an AI-native engineer: Has integrated AI-assisted development tools meaningfully into their workflow and can articulate specific ways those tools have expanded the scope or quality of what they deliver.
Skills — Core Technical Capabilities
Required
- 4+ years building and maintaining production backend services or platforms
- Proficiency in at least one modern backend language (Node.js, Go, Python, Ruby, or equivalent)
- Experience designing services that process high-volume data — ingestion, transformation, or analysis at scale
- Ability to define clean API boundaries and reason clearly about system design tradeoffs
- Solid practices around testing, monitoring, and incident response as first-class engineering concerns
- Strong communicator who collaborates effectively with engineers, PMs, and designers across a multidisciplinary team
Preferred
- Exposure to distributed systems, high-availability platforms, or large-scale data pipelines
- Experience with cloud or containerized environments (AWS, Docker, Kubernetes)
Competencies — Behaviors We Like to See
Ownership without prompting
- Takes features from design to production and follows through on monitoring, iteration, and cleanup — without waiting to be asked
- When something breaks or degrades, moves toward the problem rather than waiting for assignment
Platform thinking over short-term fixes
- Makes system design decisions that account for how other teams will consume and build on the work
- Actively flags technical debt and advocates for the right tradeoff between speed and long-term health
AI-native craft
- Has a specific, credible point of view on how AI tools have changed their engineering practice — not just faster, but differently scoped
- Brings intellectual curiosity to tooling and is willing to experiment, evaluate, and share what works
Collaborative standard-setting
- Uses code review as a genuine craft conversation — gives feedback that makes the code and the engineer better
- Invests in pairing and knowledge sharing with junior teammates without being asked
Our Laurels
- AdAge Best Places to Work
- ThinkLA Partner of the Year
- Built In Best Places to Work
- Cynopsis 2025 Top Women in Media - Jeannine Shao Collins
- Martech Breakthrough Awards - Best Overall Adtech Company
- Digiday Media Awards Best Event
- Cynopsis Media Impact Awards-Best CTV Platform
- Martech Breakthrough Awards-CTV Innovation
- Adweek Media Plan of the Year Awards - Best Use of Insights
Follow Our Lead
- Big Picture: kargo.com
- The Latest: Instagram (@kargomobile) and LinkedIn (Kargo)
Kargo is an Equal Opportunity Employer. We are committed to building an inclusive and diverse workplace where all employees and applicants are treated with respect and dignity. We do not discriminate on the basis of race, color, ethnic origin, religion or belief, sex, sexual orientation, gender identity or expression, age, disability, marital or family status, national origin, veteran status, or any other characteristic protected by applicable local, state, or federal law. All qualified applicants will receive consideration for employment.
Pursuant to applicable fair chance laws, including the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Kargo will consider qualified applicants with arrest and conviction records for employment.
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- Node.js
- Go
- Python
- Ruby
- AWS
- Docker
- Kubernetes
- Distributed Systems
- API Design
- Data Pipelines
Возможные вопросы на собеседовании
Проверка опыта работы с масштабируемыми системами, что критично для команды CDA.
Расскажите о самом сложном конвейере обработки данных (data pipeline), который вы проектировали. С какими узкими местами вы столкнулись?
Вакансия делает упор на 'AI-native' подход. Работодателю важно понять, как вы используете ИИ для улучшения качества кода.
Как именно вы интегрировали инструменты ИИ в свой процесс разработки? Приведите пример, когда это помогло решить сложную задачу иначе или быстрее.
В описании упоминается важность чистых API и долгосрочного здоровья платформы.
Как вы подходите к проектированию границ API, когда несколько команд должны использовать ваш сервис? Как вы балансируете между скоростью поставки и техническим долгом?
Роль предполагает полную ответственность за фичи.
Опишите случай, когда вы обнаружили проблему в продакшене до того, как о ней сообщили пользователи. Какие меры по мониторингу вы предприняли?
Команда ценит культуру код-ревью как способ обучения.
Каков ваш подход к проведению код-ревью? Как вы даете конструктивную обратную связь менее опытным коллегам?
Похожие вакансии
Software Engineer - Python, Automation
Software Engineer (IntelliJ Platfrom Licensing)
Intermediate Java Developer (Big Data)
Intermediate Java Developer
Software Engineer - Python/ Rust
Front Office Core Developer – Crypto
1000+ офферов получено
Устали искать работу? Мы найдём её за вас
Quick Offer улучшит ваше резюме, подберёт лучшие вакансии и откликнется за вас. Результат — в 3 раза больше приглашений на собеседования и никакой рутины!
- Страна
- Ирландия