ZimZee Recruiting is seeking a Data Engineer to join our client’s dynamic team in Lehi, Utah. This is a fast-paced research and development environment of medical devices. The ideal candidate will have strong experience as a Data Engineer, with a strong emphasis on designing, building, and optimizing various types of data pipelines, data warehousing, and ETL processes.

As a key member of the team, the Senior Data Engineer will play a crucial role in managing and enhancing our data infrastructure to support data-driven decision-making and innovation in the biomedical field.

Skills & Requirements:

• Bachelor's or Master's degree in Data Engineering, Computer Science, Data Science, or a related field.
• 4 years of professional experience as a data engineer
• Experience with cloud platforms, including AWS and Azure. Google Cloud and Snowflake would be nice but not required
• Expertise in designing, building, and optimizing data pipelines, with a focus on data warehousing and ETL processes.
• 4 years of experience with Python and strong familiarity with data engineering python libraries
• Proficiency in C# and SQL
• 3-5 years of optimization experience
• 2 years of experience as a Data Pipeline Architect
• LMOPS experience
• In-depth knowledge of database systems (SQL and NoSQL) and containerization technologies (e.g., Docker).
• Experience with other data engineering utilities like but not limited to Apache spark, Kafka, Hadoop

Key Responsibilities:

• Data Warehouse Design and Implementation: Design and implement scalable and efficient data warehousing solutions on cloud platforms such as AWS, Google Cloud, Azure, and Snowflake.
• ETL Architecture: Develop and optimize end-to-end ETL processes, ensuring the seamless flow of data from source to destination while meeting performance and scalability requirements.
• Multi-Cloud Expertise: Demonstrate proficiency in working with various cloud providers, selecting the most suitable services for each scenario, and managing data engineering solutions in a multi-cloud environment.
• Tool and Software Selection: Evaluate, recommend, and implement tools and software specific to each cloud platform, ensuring alignment with the unique requirements of biomedical research and development.
• Code Management and Updates: Take ownership of continuous integration and deployment processes, manage codebase updates, and ensure the reliability and stability of data engineering solutions across different cloud environments.
• Collaboration with Cross-functional Teams: Work closely with data scientists, researchers, and other cross-functional teams to understand their data requirements and provide effective data engineering solutions, considering the nuances of biomedical data.
• Performance Monitoring and Troubleshooting: Implement monitoring systems to track the performance of data pipelines, promptly identify and resolve issues, and optimize processes for efficiency across multiple cloud platforms.