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Senior Analytics Engineer (Data Modeling & BI)

AI Summary
Fever is seeking a Senior Analytics Engineer to build reliable, well-modeled data products for business analytics and self-service reporting. The role combines SQL and dbt data modeling with collaboration across data engineering, B2B, marketing, CRM, and product teams. The successful candidate will contribute to data quality, governance, observability, BI enablement, and Python-based Airflow workflows. This remote Spain position requires fluency in both Spanish and English and comfort working in a fast-paced international data organization. Experience with cloud warehouses, BI tools, and federated or data mesh environments is particularly relevant.
Remote from
Spain
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
13 Dec 2026
Listing views
56
Application actions
3
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Opportunity details

About this role.

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 +55 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 a Senior 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.
  • Leverage Agentic AI, Claude Code, and other advanced AI tools in your day-to-day workflow to accelerate development cycles, boost efficiency, and maximize your overall impact.

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.
  • Hands-on experience or strong interest in leveraging Agentic AI and coding assistants (like Claude Code or Copilot) to accelerate data workflows and development.

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

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a senior, cross-functional data role requiring strong modeling judgment, production-oriented analytics engineering practices, and the ability to align technical assets with business metrics. The scope spans multiple domains and platforms, increasing both technical and stakeholder complexity.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive
$125,000
US market range (per year)$105k–$145k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer base-pay amount is disclosed. The yearly cadence is inferred from the senior full-time professional role; the figures are estimated annual USD market compensation, not employer-provided pay.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a dbt model for a business-critical revenue metric used by several teams?

I would first align on the metric definition, grain, source systems, adjustment rules, and ownership with finance and business stakeholders. I would build layered staging, intermediate, and mart models, document the semantic definition, add dbt tests for uniqueness, referential integrity, and accepted values, and publish a certified model for BI use.

What steps do you take when different teams report different values for the same KPI?

I investigate the metric definitions, source freshness, filters, grains, and transformation logic before comparing results. I then facilitate agreement on a canonical definition, implement it in a governed shared model, document assumptions, and help teams migrate their dashboards to the certified metric.

Describe how you would improve data quality in a growing analytics platform.

I would prioritize critical datasets and introduce automated checks for freshness, volume anomalies, schema changes, uniqueness, and business-rule validity. I would pair these checks with ownership, alerting, incident procedures, lineage documentation, and regular reviews of recurring data issues.

How have you used Airflow or another orchestration tool to support analytics engineering?

I use orchestration to coordinate source ingestion, transformation dependencies, quality checks, and downstream refreshes. I focus on idempotent tasks, clear retries and alerts, observable runs, and dependency design that keeps critical reporting datasets fresh without creating unnecessary pipeline coupling.

How do you translate an ambiguous stakeholder request into a reusable data asset?

I begin with discovery questions about the decision being made, users, required dimensions, metric definitions, refresh expectations, and success criteria. Rather than building a one-off extract, I identify reusable entities and measures, model them with clear documentation, validate the output with stakeholders, and enable them through an appropriate BI interface.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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