Data Engineer

(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. - 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). - 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.