• To be successful in this role, you must balance technical data engineering expertise with exceptional communication skills with technical and non technical stakeholders to ensure successful project execution and customer satisfaction
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field with 6+ years of data engineering experience
• ETL or Pipeline design/implementation with Large Datasets
• Experience with distributed processing systems like Spark, Kubernetes, Elasticsearch, and familiarity with operational visibility tools like Prometheus, Loki and Grafana
• Fluency required with SQL and Python with bonus points for also being proficient in Java
• Experience in AWS and GCP cloud environments
• Prior customer facing experience with demonstrable ability to communicate effectively with both technical and non-technical stakeholders and partners at all levels
• Experience building relationships with and collaborating cross functionally across Product, Engineering and AI teams to achieve customer outcomes

Desirable

• As a Senior Data Engineer in Mendel’s customer delivery team, you will help architect and implement solutions allowing our customers to fully realize value from the Mendel AI platform
• You will serve as a primary technical point of contact to the customer, working with them during post-sales implementation to ensure we support their end to end clinical data analytical needs powered by Mendel's AI capabilities
• Navigate and assess customer's complex data infrastructure landscape across various cloud environments including AWS, Azure and GCP to to build a deployment solution design
• Design, develop and support data pipelines and workflows that integrate various sources of structured and unstructured medical data across our customers' landscape with the Mendel AI platform
• Assess and implement any customizations and custom data wrangling needed to transform customer datasets in a way that is best suited for processing by our ML models
• Monitor data processing and machine learning workflows to ensure customer data is successfully processed by our ML models, debugging and resolving any issues faced along the way
• Generate necessary data artifacts and analytics needed to assess the quality of results generated by different parts of the AI workflow to ensure they meet desired quality standards
• Collaborate closely with the AI team to assess model performance and improve results as needed
• Collaborate closely with Product Engineering as needed to resolve any issues faced during customer deployment and onboarding
• Collaborate closely with Customer Success Managers and Account managers to maintain close customer relationships and ensure customer satisfaction
• Develop tooling and playbooks to streamline and automate key technical delivery activities to be repeatable and scalable