Data Engineer en Barcelona, España

• A Bachelor's degree in Mathematics, Engineering, Telecommunications, or a related field

• 3-5 years of experience working as a Data Analyst, Data Scientist or Data Engineer.

• Technical Proficiency: Expertise in SQL and Python for data manipulation, analysis, and automation.

• Analytical & Strategic Thinking: Proven ability to dissect complex problems and deliver strategic solutions.

• Communication & Storytelling: Strong ability to distill intricate technical concepts into clear, actionable business insights.

• Proactive Mindset: A detail-oriented, solution-focused professional with high standards for data accuracy.

• Collaboration: Dependable team player who thrives both independently and in collaborative environments.

• A positive, energetic attitude that aligns with our SHAKE culture:

  • Say things as they are

  • Have fun

  • Always people first

  • Keep rowing together

  • Es posible

• Workflow Orchestration: Hands-on experience with orchestration tools like Apache Airflow.

• Data Visualization: Hands-on experience building reports and dashboards in tools such as Power BI, Tableau, or QlikView.

• Version Control: Strong understanding of version control best practices using Git.

• Cloud Platforms: Previous experience working within cloud environments, specifically AWS, is highly appreciated.

Are you a data enthusiast ready to make an impact? Join our team as a Data Engineer, where you'll play a pivotal role in shaping our company's future through sharp analysis, strategic insight, and proactive collaboration.

• Develop and implement end-to-end projects powered by advanced AI tools to solve and automate key business problems and processes.

• Develop and deploy traditional machine learning models (Classification/Regression).

• Foster an AI-driven culture within Holaluz by researching and disseminating AI knowledge across the company.

• Stay up to date with emerging technologies and assess their suitability for Holaluz.

• Populate the Data Warehouse with datasets using Python and SQL to create robust data pipelines.

• Optimize and evolve the data warehousing infrastructure to ensure cost-effectiveness and scalability.

• Maintain the smooth and efficient operation of Data Analytics systems.

• Answer business questions using a wide range of data techniques.

• Develop concise one-page documents with actionable insights and business recommendations.

• Conduct in-depth analyses to solve key business problems.

• Translate complex data into clear and concise presentations for senior management and stakeholders, effectively communicating key findings and recommendations.

• Create compelling visualizations to effectively communicate key metrics to stakeholders.