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.

1. 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.

1. 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).

1. 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.