Data Engineer

This mid-level Data Engineer role requires a solid foundation in data engineering principles supported by a bachelor's degree, indicating a strong academic background in relevant fields such as computer science, information technology, or related disciplines. With 40 experience points, the candidate is expected to have practical experience designing, building, and maintaining scalable data pipelines and architectures. Key technical skills likely include proficiency in programming languages commonly used in data engineering such as Python, SQL, and possibly Java or Scala. Experience with big data technologies and frameworks like Apache Hadoop, Spark, Kafka, and cloud platforms (AWS, Azure, or Google Cloud) is typically essential to handle large-scale data processing and storage solutions. Familiarity with ETL (Extract, Transform, Load) processes, data warehousing concepts, and database management systems (both SQL and NoSQL) is also important. The role demands strong problem-solving abilities, data modeling expertise, and the capability to optimize data workflows for performance and reliability. Additionally, knowledge of containerization tools (Docker, Kubernetes) and version control systems (Git) may be expected to support development and deployment processes. Collaboration skills are crucial, as Data Engineers often work closely with data scientists, analysts, and software engineers to ensure data accessibility and integrity. Overall, this position suits a candidate with a balanced mix of formal education and hands-on experience in modern data engineering technologies and best practices.