Data Engineer Revenue Operations

<p>We are looking for a <strong>Mid-Level Data Engineer</strong> to join our <strong>Revenue Operations</strong> team, responsible for building, scaling, and maintaining data pipelines that support strategic revenue decisions.</p> <p>This role plays a key part in connecting data across <strong>Marketing, Sales, Customer Success, and Finance</strong>, ensuring high data quality, reliability, and availability.</p> <p>The position requires <strong>on-site presence in Madrid</strong>, with close collaboration across cross-functional teams in a fast-paced and constantly evolving environment.</p> <p>&nbsp;</p> <h3><strong>Responsibilities</strong></h3> <h4><strong>1. Data Engineering (Core)</strong></h4> <ul> <li>Design, build, and maintain <strong>scalable and reliable data pipelines</strong> (ETL/ELT).</li> <li>Develop and optimize <strong>analytical data models</strong> (bronze, silver, and gold layers).</li> <li>Ensure <strong>data quality, governance, and consistency</strong>.</li> <li>Monitor pipelines, proactively identify bottlenecks, and resolve failures.</li> <li>Work with large volumes of structured and semi-structured data.<br><br></li> </ul> <h4><strong>2. Revenue Operations</strong></h4> <ul> <li>Integrate data from multiple sources, including:</li> <ul> <li>CRM systems (e.g., HubSpot)</li> <li>Marketing platforms</li> <li>Financial and billing systems (SAP)</li> <li>Product data sources<br><br></li> </ul> <li>Build datasets to support analysis of:</li> <ul> <li>Sales funnel and pipeline</li> <li>Revenue forecasting</li> <li>Recurring revenue (MRR, ARR)</li> <li>Churn, retention, and expansion</li> <li>Performance metrics for SDRs, AEs, and CSMs<br><br></li> </ul> <li>Support the development of strategic KPIs and metrics for leadership and C-level stakeholders.</li> <li>Partner closely with data analysts, RevOps, and business teams.<br><br></li> </ul> <h4><strong>3. Technology &amp; Tools</strong></h4> <ul> <li>Use <strong>Databricks</strong> for data processing, transformation, and orchestration.</li> <li>Work extensively with <strong>advanced SQL</strong> and <strong>Python</strong>.</li> <li>Leverage the <strong>Google ecosystem</strong>, including:</li> <ul> <li>BigQuery</li> <li>Google Cloud Storage</li> <li>Google Sheets (automation and integrations)</li> </ul> <li>Enable BI tools and dashboards (e.g., Looker, Power BI, Tableau).<br><br></li> </ul> <h4><strong>4. Collaboration &amp; Environment</strong></h4> <ul> <li>Collaborate closely with business teams, translating requirements into technical solutions.</li> <li>Participate actively in agile ceremonies (planning, daily stand-ups, reviews).</li> <li>Thrive in a <strong>dynamic, high-growth, and fast-changing environment</strong>.</li> <li>Continuously propose improvements in architecture, processes, and performance.</li> </ul> <p>&nbsp;</p> <h3><strong>&nbsp;Requirements</strong></h3> <ul> <li>Proven experience as a <strong>Mid-Level Data Engineer</strong>.</li> <li>Strong expertise in <strong>SQL</strong> (data modeling and performance optimization).</li> <li>Solid experience with <strong>Python</strong> for data engineering.</li> <li>Hands-on experience with <strong>Databricks</strong>.</li> <li>Experience with <strong>Google Cloud Platform</strong> (BigQuery, GCS).</li> <li>Previous experience in&nbsp;<strong>Revenue Operations</strong>, Sales, or Finance.</li> <li>Knowledge of&nbsp;<strong>SaaS metrics</strong> (MRR, ARR, LTV, CAC, churn).</li> <li>Strong understanding of:</li> <ul> <li>ETL / ELT processes</li> <li>Data Warehousing and Data Lakes</li> <li>Dimensional data modeling</li> </ul> <li>Experience with version control systems (Git).</li> </ul> <h3><strong>Nice to Have</strong></h3> <ul> <li>Experience with BI tools.</li> <li>International work experience.</li> <li>Fluence in Spanish.</li> <li>Advanced English it's good.</li> </ul>