Data Engineer and Advanced Analytics Engineer

Description

Job Summary: We are seeking a data professional to join our team, enhance analytical capabilities, design and implement data management systems and analytical dashboards, and optimize information processes in both on-premises and cloud environments. Key Highlights: 1. Design and implementation of large-scale data management systems 2. Interdisciplinary collaboration for clear data strategies 3. Use of tools such as Microsoft Fabric, Snowflake, and Power BI We seek a data professional to integrate into our team and enhance analytical capabilities. Key responsibilities include designing and implementing large-scale data management systems and advanced analytical dashboards. Information ingestion, transformation, and modeling processes will be optimized across on-premises and cloud environments. The goal is to build and deploy efficient and secure solutions using tools such as Microsoft Fabric, Snowflake, and Power BI. Close collaboration with other departments will be essential to translate business and financial requirements into clear data strategies, establishing robust databases that enable organization-wide access to information. Maintaining high standards of quality and compliance across all analytical activities is critical. Knowledge sharing and promotion of best practices to expand the team’s data engineering skills are also expected. A solution-oriented and proactive attitude—proposing improvements and new data-driven solutions that deliver business value—is highly valued. Professional fluency in English (written and spoken) is required for collaboration in international environments. A university degree (Bachelor’s, Licentiate, or Technical Engineering) in Mathematics, Engineering, Economics, or a related field—with a strong quantitative analytics focus—is mandatory. Advanced proficiency in Microsoft Excel—including data modeling, complex formulas, and advanced analytics—is expected. Advanced SQL skills—focused on querying, transforming, and optimizing large volumes of data—are essential. Solid understanding of ETL (extraction, transformation, loading) processes—including integration with diverse sources and destinations—is desirable. Advanced experience with SAP—particularly table structures and data models—as well as experience in Finance or corporate financial processes—is a plus. Familiarity with Microsoft Fabric and/or Snowflake is advantageous. Knowledge of programming languages for data science or engineering (e.g., Python, R), and specialized certifications or academic projects in advanced analytics, are also valued.