• Build and support scalable data engineering pipelines (load, extract, clean, transform, ingest, etc.) for our SaaS AI applica ons and Predic ons-as-a-Service implementa on and products. 
• Build and deploy data engineering pipelines at scale using Kubeflow/Airflow. 
• Own mul ple data pipelines. • Review, audit, and improve data engineering pipelines. • Document and communicate technical and func onal designs (HLD, LLD), melines, and plans. 
• Collaborate closely with AI/ML, DevOps, MLE, and other engineering and product teams. • Work with engineering and implementa on teams to understand business problems and implement efficient so\ware solu ons. 
• Follow configurable programming prac ces (config files, modular programming, parameteriza on, etc.). 
• Support the maintenance and enhancement of deployed pipelines, including bug fixes and performance improvements. 
• Par cipate in organiza on-building ac vi es (cross-team training, hiring, etc.) to further the Asper mission and culture. 

What makes you a great fit:

• Proficient coding skills in Python, PySpark, and SQL. 
• Proven track record of wri ng and maintaining Spark code in a produc on environment. 
• Experience building and suppor ng scalable data engineering pipelines (ETLs), including data extrac on, loading, transforma on, real-:me monitoring, and aler ng. 
• Familiarity with orchestra on technologies such as Airflow and Kubeflow. 
• Experience with public clouds (AWS/Azure). 
• Demonstrated produc on experience in big data infrastructure and data modelling. 
• Performance opmiza on experience for data loading and inges on. 
• Knowledge of essen al developer tools, including GitHub, Docker, and VSCode. • Ability to thrive in a fast-paced, deadline-driven environment.