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Business Intelligence Engineer
Fever — это быстрорастущий 'единорог' с современным стеком технологий (Snowflake, DBT, Airflow). Предложение включает опционы на акции, гибкий график и отличный социальный пакет, что делает вакансию очень привлекательной.
Сложность вакансии
Роль требует глубоких знаний SQL и DBT, а также навыков программирования на Python для работы с Airflow. Процесс отбора включает 90-минутное техническое интервью и проверку владения двумя языками (испанским и английским).
Анализ зарплаты
Зарплата в объявлении не указана, но для позиции Analytics/BI Engineer в Мадриде рыночный диапазон составляет от 45,000 до 65,000 евро в год. Fever позиционирует себя как лидер рынка, поэтому можно ожидать конкурентоспособную оплату плюс бонусы и опционы.
Сопроводительное письмо
I am writing to express my strong interest in the Business Intelligence Engineer position at Fever. With a solid background in SQL, DBT, and Python, I am excited about the opportunity to bridge the gap between data engineering and business analytics in such a fast-paced, data-driven environment. My experience in building robust data models and maintaining Airflow pipelines aligns perfectly with your mission to democratize access to culture through cutting-edge technology.
Throughout my career, I have focused on transforming raw data into actionable insights that drive business growth. I am particularly impressed by Fever's federated data organization and your commitment to a data mesh vision. I am confident that my technical skills in Snowflake and Metabase, combined with my collaborative mindset, will allow me to contribute effectively to your B2B, Marketing, and Product squads while ensuring high data quality and governance standards.
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Описание вакансии
Hey there!
We’re Fever, the world’s leading tech platform for culture and live entertainment,
Our mission? To democratize access to culture and entertainment. With our proprietary cutting-edge technology and data-driven approach, we’re revolutionizing the way people engage with live entertainment.
Every month, our platform inspires over 300 million people in +40 countries (and counting) to discover unforgettable experiences while also empowering event creators with our data and technology, helping them scale, innovate, and enhance their events to reach new audiences.
Our results? We’ve teamed up with major industry leaders like Netflix, F.C. Barcelona, and Primavera Sound, presented international award-winning experiences, and are backed by several leading global investors! Impressive, right?
To achieve our mission, we are looking for bar-raisers with a hands-on mindset who are eager to help shape the future of entertainment!
Ready to be part of the experience?
Now, let’s discuss this role and what you will do to help achieve Fever’s mission.
About the Role
At Fever, we are building a federated data organization that combines centralization where it’s essential and autonomy where it creates value. Our teams leverage modern data platforms (Snowflake, DBT, Airflow, DataHub, Metabase, etc.). At the same time, we are developing our own technology to deliver high-quality data products across multiple business domains.
As an Analytics Engineer, you’ll be the bridge between data engineering and business analytics, ensuring that our stakeholders can make decisions on reliable, well-modeled, and discoverable datasets.
What You’ll Do
- Design, build, and maintain data models (DBT, SQL) that transform raw data into clean, trusted datasets for self-service and reporting.
- Collaborate with the data engineering team to define and certify business-critical metrics (revenue, engagement, marketing performance, B2B KPIs, etc.).
- Work with business squads (B2B, Marketing, CRM, Product) to understand their needs and turn them into reusable data assets.
- Ensure data quality and consistency, partnering with the data engineering team on testing frameworks, observability, and governance.
- Support self-service analytics by enabling stakeholders to explore data confidently by leveraging existing viz and BI tools or driving custom developments to let the data tell a complete story.
- Contribute to Airflow pipelines in Python, supporting automation and orchestration needs.
- Contribute to our data mesh vision, creating domain-owned datasets while following platform and engineering guidelines.
What You Bring
- Fluent in Spanish and English; you’ll communicate in Spanish with local teams and in English with global teams
- A Bachelor’s, Master’s, or PhD in Computer Engineering, Data Engineering, Data Science, or a related field.
- A collaborative mindset: comfortable working with both engineers and business stakeholders.
- Strong communication skills, adaptable to a multidisciplinary, international, and fast-paced environment.
- A keen interest in understanding a complex business landscape and to drive the company forward by providing the data to answer the most valuable questions
- Strong experience with SQL and data modeling (star/snowflake schemas, data vault, or similar).
- Hands-on experience with DBT (or similar transformation frameworks).
- Hands-on experience with Python and orchestration frameworks (Airflow, Dagster, Prefect).
- Familiarity with modern cloud data warehouses (Snowflake, BigQuery, Redshift, etc.).
- Experience with BI tools (Metabase, Superset, Tableau, etc.).
- Understanding of data quality, governance, and observability practices.
Nice to Have
- Experience in a federated or data mesh environment.
- Familiarity with data catalog tools (DataHub, Collibra, Amundsen).
- Exposure to analytics in domains like marketing, CRM, or B2B reporting.
Benefits & Perks
- Attractive compensation package consisting of base salary and the potential to earn a significant bonus for top performance
- Stock options
- Responsibility from day one and professional and personal growth
- Opportunity to have a real impact in a high-growth global category leader
- 40% discount on all Fever events and experiences
- Home office friendly
- Health insurance and other benefits such as Flexible remuneration with a 100% tax exemption through Cobee
- English Lessons
- Gympass Membership
- Possibility to receive in advance part of your salary by Payflow
Interview Process
The interview process will be conducted in Spanish; however, some interviews will require English.
- Talent Interview
- Technical Interview (90')
- Manager Interview
Thank you for considering joining Fever. We cannot wait to learn more about you!
If you want to learn more about us: Fever's Blog | Tech.Eu |TechCrunch
Fever is committed to creating an inclusive and diverse workspace where everyone's background and ideas count. Our main goal is to find the best possible talent regardless of place of birth, racial or ethnic origin, gender, gender identity, religion, opinion, sexual orientation, disability, pregnancy, marital status, age or caring responsibilities. We encourage everyone to apply!
If you require any kind of accommodation during the selection process please contact our Talent team so we can help you by providing a welcoming and seamless journey.
If you want to know more about how Fever processes your personal data, click here Fever - Candidate Privacy Notice
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Навыки
- SQL
- dbt
- Python
- Airflow
- Snowflake
- Metabase
- Tableau
- BigQuery
- Redshift
- DataHub
- Dagster
- Prefect
- Superset
Возможные вопросы на собеседовании
Проверка навыков проектирования архитектуры данных для аналитики.
Расскажите о вашем опыте проектирования схем типа 'звезда' или 'снежинка'. Какие факторы вы учитываете при выборе структуры данных для BI-инструментов?
DBT является ключевым инструментом в стеке компании.
Как вы организуете тестирование и документацию в DBT проектах для обеспечения качества данных?
Вакансия предполагает работу с Airflow.
Опишите случай, когда вам приходилось оптимизировать сложный DAG в Airflow. С какими проблемами производительности вы сталкивались?
Роль Analytics Engineer подразумевает тесное взаимодействие с бизнесом.
Как вы подходите к сбору требований от нетехнических стейкхолдеров (например, из отделов маркетинга или CRM)?
Компания движется в сторону Data Mesh.
Каково ваше понимание концепции Data Mesh и как, по вашему мнению, она должна влиять на работу инженера по аналитике?
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