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

• 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.
• 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.
• 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).
• 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.