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.