Data Engineer Revenue Operations

We are looking for a Mid-Level Data Engineer to join our Revenue Operations team, responsible for building, scaling, and maintaining data pipelines that support strategic revenue decisions.

This role plays a key part in connecting data across Marketing, Sales, Customer Success, and Finance, ensuring high data quality, reliability, and availability.

The position requires on-site presence in Madrid, with close collaboration across cross-functional teams in a fast-paced and constantly evolving environment.

Responsibilities

1. Data Engineering (Core)

• Design, build, and maintain scalable and reliable data pipelines (ETL/ELT).


• Develop and optimize analytical data models (bronze, silver, and gold layers).


• Ensure data quality, governance, and consistency.


• Monitor pipelines, proactively identify bottlenecks, and resolve failures.


• Work with large volumes of structured and semi-structured data.
  
  

2. Revenue Operations

• Integrate data from multiple sources, including:




• CRM systems (e.g., HubSpot)


• Marketing platforms


• Financial and billing systems (SAP)


• Product data sources
  
  




• Build datasets to support analysis of:




• Sales funnel and pipeline


• Revenue forecasting


• Recurring revenue (MRR, ARR)


• Churn, retention, and expansion


• Performance metrics for SDRs, AEs, and CSMs
  
  




• Support the development of strategic KPIs and metrics for leadership and C-level stakeholders.


• Partner closely with data analysts, RevOps, and business teams.
  
  

3. Technology & Tools

• Use Databricks for data processing, transformation, and orchestration.


• Work extensively with advanced SQL and Python.


• Leverage the Google ecosystem, including:




• BigQuery


• Google Cloud Storage


• Google Sheets (automation and integrations)




• Enable BI tools and dashboards (e.g., Looker, Power BI, Tableau).
  
  

4. Collaboration & Environment

• Collaborate closely with business teams, translating requirements into technical solutions.


• Participate actively in agile ceremonies (planning, daily stand-ups, reviews).


• Thrive in a dynamic, high-growth, and fast-changing environment.


• Continuously propose improvements in architecture, processes, and performance.

 Requirements

• Proven experience as a Mid-Level Data Engineer.


• Strong expertise in SQL (data modeling and performance optimization).


• Solid experience with Python for data engineering.


• Hands-on experience with Databricks.


• Experience with Google Cloud Platform (BigQuery, GCS).


• Previous experience in Revenue Operations, Sales, or Finance.


• Knowledge of SaaS metrics (MRR, ARR, LTV, CAC, churn).


• Strong understanding of:




• ETL / ELT processes


• Data Warehousing and Data Lakes


• Dimensional data modeling




• Experience with version control systems (Git).

Nice to Have

• Experience with BI tools.


• International work experience.


• Fluence in Spanish.


• Advanced English it's good.