Responsibilities
- Design, develop, and maintain robust and scalable ETL and ELT data pipelines.
- Create and refine multi-layered analytical data models, including raw, cleansed, and aggregated layers.
- Enforce standards for data quality, governance, and uniformity across systems.
- Monitor data workflows, detect performance issues early, and troubleshoot failures.
- Process and manage large datasets with both structured and semi-structured formats.
- Consolidate data from diverse platforms such as CRM tools, marketing software, financial systems like SAP, and product analytics sources.
- Construct datasets to analyze key business areas including sales pipelines, revenue projections, recurring revenue (MRR, ARR), churn, retention, expansion, and team performance for sales and customer success roles.
- Assist in defining strategic performance indicators for executive leadership.
- Collaborate with data analysts, revenue operations, and business units to align data solutions with operational needs.
- Utilize Databricks for data transformation, processing, and workflow orchestration.
- Apply advanced SQL and Python programming for data manipulation and automation.
- Work within the Google Cloud ecosystem, including BigQuery, Google Cloud Storage, and automated integrations with Google Sheets.
- Support and enhance business intelligence platforms and interactive dashboards using tools like Looker, Power BI, or Tableau.
- Translate business requirements into technical data implementations through close collaboration with stakeholders.
- Engage in agile development practices, including sprint planning, daily stand-ups, and retrospective meetings.
- Adapt quickly in a fast-paced, evolving, high-growth environment.
- Propose and implement ongoing enhancements to data architecture, processing efficiency, and system performance.
Requirements
- Mid-level professional experience as a Data Engineer.
- Advanced proficiency in SQL, including data modeling and query optimization.
- Demonstrated use of Python in data engineering tasks.
- Practical experience working with Databricks for data processing.
- Familiarity with Google Cloud Platform services such as BigQuery and Google Cloud Storage.
- Background in Revenue Operations, Sales, or Financial domains.
- Understanding of key SaaS metrics including MRR, ARR, LTV, CAC, and churn.
- Solid foundation in ETL and ELT workflows, data warehousing, data lakes, and dimensional modeling.
- Experience using Git or similar version control systems.
Nice to Have
- Exposure to business intelligence tools for reporting and visualization.
- Professional experience in international or global teams.
- Spanish language proficiency.
- Fluent English communication skills.
Work Arrangement
On-site — Madrid
