Requisitos:

• Expert in designing and maintaining scalable data architectures, capable of handling complex data systems.
• Proficient in SQL and other data manipulation languages, with a strong ability to optimize and troubleshoot queries.
• Skilled in DevOps practices and continuous integration to enhance workflow efficiencies.
• Experienced in software development methodologies, contributing to both project and operational goals.
• Knowledgeable in data structures and algorithms, enhancing data processing and storage solutions.
• Familiar with distributed data technologies like Spark and Hadoop, capable of managing large-scale data operations.
• Efficient in system integration, ensuring seamless data flow and connectivity across platforms.
• Proficient in programming languages such as Python, Scala, Java, and C++, applying them effectively in data engineering tasks.
• Experienced in SQL database management, performance tuning, and complex query handling.
• Skilled in cloud platform deployment and tools like Kubernetes, optimizing cloud-based data solutions.
• Capable of performing advanced troubleshooting and guiding others through complex problem-solving processes.

Funciones:

• Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals
• Define data requirements, gather and mine large-scale structured and unstructured data, and validate data using various data tools in the Big Data Environment
• Support Data Scientists in data sourcing and preparation to visualize data and synthesize insights of commercial value
• Lead the evaluation, implementation, and deployment of emerging tools and processes for analytic data engineering to improve productivity
• Develop and deliver communication and education plans on analytic data engineering capabilities, standards, and processes
• Partner with business analysts and solutions architects to develop technical architectures for strategic enterprise projects and initiatives
• Mentor and coach other data and analytic professionals on data standards and practices