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Lead Data Engineer at LightWork AI
Исключительная возможность для опытного инженера стать первым лицом в отделе данных AI-стартапа с подтвержденным спросом (14к юнитов в пилоте) и сильным руководством.
Сложность вакансии
Высокая сложность обусловлена ролью 'foundational hire', требующей более 7 лет опыта, умения строить системы с нуля и глубоких знаний в области векторных баз данных и RAG.
Анализ зарплаты
Для позиции Lead Data Engineer в Лондоне в AI-стартапе на стадии Series A/B рыночная зарплата обычно находится в диапазоне £90,000–£130,000 плюс опционы. Данная роль является ключевой, поэтому компенсация должна соответствовать верхним границам рынка.
Сопроводительное письмо
I am writing to express my strong interest in the Lead Data Engineer position at LightWork AI. With over 7 years of experience in building distributed data systems and a deep proficiency in Python and PostgreSQL, I am excited about the opportunity to architect the data backbone for Felicity. My background in designing scalable pipelines and working with modern data stacks aligns perfectly with your mission to automate property management workflows.
In my previous roles, I have successfully led the development of complex data infrastructures from scratch, ensuring high reliability and observability. I am particularly drawn to LightWork AI because of the challenge of implementing vector databases and RAG systems to support core AI products. Working under the leadership of Spyridon Katsoulis and contributing to a product with such significant market potential is a unique opportunity that I am eager to pursue.
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Откликнитесь в jack-jill-external-ats уже сейчас
Станьте первым ведущим инженером данных в перспективном AI-стартапе и постройте архитектуру будущего для управления недвижимостью!
Описание вакансии
This is a job that Jill, our AI Recruiter, is recruiting for on behalf of one of our customers.
She will pick the best candidates from Jack's network
The next step is to speak to Jack.
Job Title: Lead Data Engineer
Company Description: LightWork AI - VC-backed AI startup building the property management operating system
Job Description:
As the first senior data hire at LightWork AI, you will architect the data backbone for Felicity, an AI assistant automating 80-90% of property management workflows. You will design scalable pipelines, vector databases, and ML infrastructure, moving the company toward revenue in 2026 within a £33B market undergoing massive structural automation.
Location: London, UK
Why this role is remarkable:
- Rare opportunity to join as the foundational data lead, owning the entire architecture and strategy for a high-growth AI startup with 14,000 units already in pilot.
- Work directly under the Head of Engineering, Spyridon Katsoulis, who previously scaled a 140-person data department and led a $35M Series B.
- High-impact environment where your work directly enables an AI product that automates 75% of administrative tasks in a massive, underserved industry.
What you will do:
- Architect and build scalable real-time and batch data pipelines to support core AI products and operational workloads.
- Design and optimize systems for vector search, retrieval-augmented generation (RAG), and machine learning data workflows.
- Establish engineering standards for data reliability, observability, and governance while mentoring the future data team as the company scales.
The ideal candidate:
- 7+ years of experience in data or backend engineering with a proven track record of building distributed data systems from scratch.
- Deep technical proficiency in Python and hands-on experience with PostgreSQL, NoSQL, and vector databases like Qdrant or Milvus.
- Strong architectural thinker who thrives in fast-moving startup environments and has experience with modern frameworks like Airflow, Kafka, or Spark.
Who are Jack & Jill?
Ok, I'll go first. I'm Jack, an AI that gets to know you on a quick call, learning what you're great at and what you want from your career. Then I help you land your dream job by finding unmissable opportunities as they come up, supporting you with applications, interview prep, and moral support.
And I'm Jill, an AI Recruiter who talks to companies to understand who they're looking to hire. Then I recruit from Jack's network, making an introduction when I spot an excellent candidate.
Next steps
Step 1. Visit our website.
Step 2. Click 'Talk to Jack'.
Step 3. Talk to Jack so he can understand your experience and ambitions.
Step 4. Jack will make sure Jill (the AI agent working for the company) considers you for this role.
Step 5. If Jill thinks you're a great fit and her client wants to meet you, they will make the introduction.
Step 6. If not, Jack will find you excellent alternatives. All for free.
We never post fake jobs
This isn't a trick. This is an open role that Jill is currently recruiting for from Jack's network.
Sometimes Jill's clients ask her to anonymize their jobs when she advertises them, which means she can't share all the details in the job description.
We appreciate this can make them look a bit suspect, but there isn't much we can do about it.
Give Jack a spin! You could land this role. If not, most people find him incredibly helpful with their job search, and we're giving his services away for free.
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Навыки
- Python
- PostgreSQL
- NoSQL
- Vector Databases
- Qdrant
- Milvus
- Airflow
- Kafka
- Spark
- RAG
- Machine Learning Infrastructure
- Distributed Systems
Возможные вопросы на собеседовании
Вакансия предполагает создание инфраструктуры с нуля для AI-ассистента.
Как бы вы спроектировали архитектуру данных для системы RAG, обеспечивающую минимальную задержку и высокую точность ответов?
Упоминается использование векторных БД (Qdrant, Milvus).
Каковы основные критерии выбора между различными векторными базами данных для масштабируемого стартапа?
Роль подразумевает лидерство и установку стандартов.
Опишите ваш подход к обеспечению качества и наблюдаемости (observability) данных в распределенных системах.
Стартап планирует выход на выручку в 2026 году и активный рост.
Как вы балансируете между скоростью разработки (Time-to-Market) и техническим совершенством архитектуры на ранних этапах стартапа?
Требуется опыт работы с Airflow, Kafka или Spark.
Расскажите о самом сложном конвейере обработки данных, который вы спроектировали: с какими узкими местами вы столкнулись и как их устранили?
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