Data Engineer

Rad AI•,

39d•$145,000 - $190,000

About The Position

Rad AI is seeking a Senior Data Engineer to join their engineering team. The company is experiencing rapid client growth and has ambitious plans, leading to the expansion of the team. The ideal candidate will play a key role in building and maintaining data collection systems, pipelines, and management tools. Rad AI is on a mission to transform healthcare with AI, with solutions revolutionizing radiology by saving time, reducing burnout, and improving patient care. Their AI has already contributed to significant improvements in radiology reports and has secured substantial funding. Their generative AI advancements are used daily by thousands of radiologists and support a significant portion of radiology groups and healthcare systems in the U.S. Rad AI is recognized as a promising healthcare AI company and is ranked among the fastest-growing companies in North America.

Requirements

5+ years relevant experience in data engineering.
Expertise in designing and developing distributed data pipelines using big data technologies on large scale data sets.
Deep and hands-on experience designing, planning, productionizing, maintaining and documenting reliable and scalable data infrastructure and data products in complex environments.
Solid experience with big data processing and analytics on AWS, using services such as Amazon EMR and AWS Batch.
Experience in large scale data processing technologies such as Spark.
Expertise in orchestrating workflows using tools like Metaflow.
Experience with various database technologies including SQL, NoSQL databases (e.g., AWS DynamoDB, ElasticSearch, Postgresql).
Hands-on experience with containerization technologies, such as Docker and Kubernetes.

Nice To Haves

Prior Software Engineering experience is a big plus.
Experience working at an early stage startup.
Experience in a HIPAA compliant environment.
Experience working on machine learning or healthcare related projects.

Responsibilities

Design and implement the data architecture, ensuring scalability, flexibility, and efficiency using pipeline authoring tools like Metaflow and large-scale data processing technologies like Spark.
Define and extend internal standards for style, maintenance, and best practices for a high-scale data platform.
Collaborate with researchers and other stakeholders to understand their data needs including model training and production monitoring systems and develop solutions that meet those requirements.
Take ownership of key data engineering projects and work independently to design, develop, and maintain high-quality data solutions.
Ensure data quality, integrity, and security by implementing robust data validation, monitoring, and access controls.
Evaluate and recommend data technologies and tools to improve the efficiency and effectiveness of the data engineering process.
Continuously monitor, maintain, and improve the performance and stability of the data infrastructure.