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Medasource seeks a Senior Technical Solutions Engineer for a hybrid role in Boston, combining hands-on coding with data ingestion and platform customization. You will develop ingestion pipelines, implement APIs, and troubleshoot complex data problems to support AI-enabled healthcare solutions.
The role requires strong OO programming, SQL, ETL/ELT, and cloud experience, with collaboration across Product, Engineering, Clinical Informatics, Data Science, and customers.
Our client is a healthcare data and technology company focused on AI-enabled precision health solutions. They operate at the intersection of technology, data science, and healthcare, using multimodal health data to create insights, models, and solutions intended to make healthcare more personalized, predictive, and precise.
The Technical Solutions Engineering organization is a key part of implementation and commercial growth strategy.
Our client is seeking a Senior Technical Solutions Engineer. This is a hands-on technical engineering role, not a traditional Solutions Architect position. The individual needs to be capable of actively writing and reviewing code, configuring/customizing products, troubleshooting technical issues, implementing APIs, and building scalable data-ingestion solutions.
A major focus of the position is data ingestion. Our client receives data from customers and partners in formats such as CSVs, spreadsheets, flat files, and other datasets. This team is responsible for how that data is received, landed, transformed through pipelines, stored in databases, curated, and ultimately made usable by clients products and downstream systems.
The formal role also owns customer implementations, troubleshooting/root-cause diagnosis, platform customization, API implementations, and cloud-based solutions that ingest, transform, store, process, and deliver data at scale.
The person should bring enough software-engineering depth to operate independently and help raise the technical capability of the team. They should be comfortable with GitHub, pull requests/code reviews, object-oriented programming, SQL, APIs, ETL/ELT pipelines, and cloud environments.
AI is also becoming increasingly important to the team. Candidates don't necessarily need formal AI/ML engineering experience, but they should demonstrate that they are actively thinking about and experimenting with how AI can improve engineering efficiency. Technical depth is more important than polished consulting experience.