.Moffatt & Nichol is renowned for specializing in large, complex waterfront infrastructure projects and is acknowledged as a global leader in this sector. We are currently seeking a Data Engineer / team in Valencia or Algeciras.The role involves supporting data infrastructure and pipelines within a large-scale cloud environment, enabling efficient data ingestion, transformation, and storage to support analytical and operational needs. This position requires implementing robust ETL processes, optimizing data flows, and ensuring adherence to data engineering best practices and standards across microservices ecosystems.About Moffatt & Nichol:Moffatt & Nichol is a premier U.S.-based global infrastructure advisor specializing in the planning and design of facilities that shape and serve our coastlines, harbors, and rivers. Demonstrating Moffatt & Nichol's dedication to design innovation since 1945, Engineering News-Record (ENR) has ranked the firm No. 1 in Marine and Port Facilities and among the Top 50 Designers in International Markets.The company's professional staff comprises engineers, planners, scientists, and architects who cater to a global clientele from offices across Europe, the Americas, and the Pacific Rim. Moffatt & Nichol delivers customized services and a standard of excellence that has become the firm's signature across three primary practice areas—coastal, environmental, and water resources; ports and harbors;and transportation, bridges, and rail.Duties and responsibilities:Reasonable accommodations may be made to enable individuals with disabilities to perform these essential functions.Design and implement big data architectures to support scalable ingestion, processing and storage large datasets efficiently by using Databricks and Azure.Design, develop, and maintain robust data pipelines to support real-time and batch processing of great volumes of data.Implement ETL processes to collect and transform data from various sources into usable formats.Optimize ETL/ELT processes to move and transform data efficiently between cloud services, data lakes, and databases.Optimize data workflows for performance, scalability, and cost-effectiveness.Monitor and troubleshoot pipeline performance, identifying and resolving issues to ensure continuous data flow.Work on database management, data storage, and data lake/warehouse solutions.Implement best practices for data governance, security, and compliance on cloud platforms.Implementation of MLOPs best practices and tools to track and productionize predictive models.Qualifications:Bachelor's degree in Computer Science, Data Engineering, or a related field.3-6 years of experience in data engineering, with a focus on cloud platforms, specifically Azure and Databricks.Strong hands-on experience with Apache Spark, Kafka, Flink, Data Lakes.Experience with NoSQL databases such as Hadoop.Experience building real-time data streaming pipelines using Kafka