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Data Engineer Revenue Operations
Job summary
A tech-driven company in Madrid is looking for a Mid-Level Data Engineer to join their Revenue Operations team. The successful candidate will design, build, and maintain data pipelines, collaborating closely with multiple teams to ensure data quality and reliability. Competence in SQL, Python, and experience with Databricks are essential. This role involves a dynamic environment with ample opportunities for strategic development, making it ideal for those seeking to make a significant impact on revenue strategies.
Qualifications
- Proven experience as a Mid-Level Data Engineer.
- Strong expertise in SQL for data modeling.
- Hands-on experience with Databricks.
Responsibilities
- Design, build, and maintain scalable data pipelines (ETL/ELT).
- Integrate data from multiple sources for revenue analysis.
- Collaborate closely with business teams for technical solutions.
Skills
Tools
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.
- 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.
- Integrate data from multiple sources, including:
- CRM systems (e.g., HubSpot)
- Marketing platforms
- Financial and billing systems (SAP)
- 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.
- Use Databricks for data processing, transformation, and orchestration.
- Work extensively with advanced SQL and Python.
- Leverage the Google ecosystem, including:
- BigQuery
- Google Sheets (automation and integrations)
- Enable BI tools and dashboards (e.g., Looker, Power BI, Tableau).
- 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.
- 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).
- Experience with BI tools.
- International work experience.
- Fluence in Spanish.
- Advanced English it's good.
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.