Data Engineer

Job Summary A Data Engineer is responsible for designing, building, and maintaining large-scale data systems that enable data-driven decision-making. They work with various stakeholders to understand data requirements, data architectures, and implement data pipelines to support business intelligence, analytics, and data science initiatives. Envíe su CV y cualquier información adicional requerida después de haber leído esta descripción, haciendo clic en el botón de solicitud. Required Experience * Bachelor's Degree in MIS/Engineering/Computer Science * Data Warehousing / BI Certification a plus * Advanced in SQL * 5+ Experience working with at least two of the top ETL tools: DBT, PWC, ODI, Datastage, DBT (mandatory) * 3+ years working with cloud environments AWS, Azure * 3+ years working with Apache Airflow * 5+ years working in an IT function * 5+ years of BI development, analyst, data modelling, and support experience * 5+ years of Relational Database Oracle, SQL Server, or * 5+ years of Columnar Database Redshift * 3+ years of Spark, Glue, EMR * 3+ Experience in scalable python development, (PySpark, Spark SQL) * 2+ Experience working with at least two of the top BI tools: Tableau, Qlik, OBIEE * Flexible to adapt and quickly (willing to) learning different technologies. Other Requirements * English C1 * Ability to cooperate and work in multicultural environment * Communication and teaching oriented, knowledge transfer ability. * Multi-tasking ability - handling multiple activities in parallel * Organized and structured * Be updated on Scrum methodology * Proactive, flexible, result-driven, with a "can do" attitude, attention to detail, problem-solving Scope of Services Data Pipeline Development * Build and maintain data pipelines to extract, transform, and load (ETL) data from various sources. Data Quality & Reliability * Implement data quality checks and validation processes. * Ensure data accuracy, consistency, and reliability across systems. System Optimization & Performance * Optimize data systems for performance, scalability, and reliability. * Troubleshoot and resolve data-related issues and system problems. Collaboration & Requirements Gathering * Work closely with data scientists, analysts, architects, and other stakeholders to understand data requirements and deliver appropriate solutions. Continuous Learning & Innovation * Stay up to date with emerging trends and technologies in data engineering. xsgfvud * Explore advancements in predictive and prescriptive modelling to drive continuous improvement. #J-18808-Ljbffr