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.