As a Senior Data Engineer, you will work within the Informatics team and contribute to Tendo’s strategic data engineering solutions by ingesting, transforming, and warehousing healthcare-related data from various sources.
You will collaborate with Tendo’s Data Scientists, Product Managers, and Machine Learning Engineers to produce quality data flows and transformations that support advanced analytics and AI/ML model development.
You will develop tools and solutions to facilitate data integration, data warehousing, and data modeling. Your work will enable Data Engineers and Data Scientists to experiment and train machine learning models to produce useful insights for Tendo’s customers.
The ideal candidate should have a strong background in software engineering, data modeling, data warehousing, ETL pipelines, and database design and a demonstrated interest or experience in applying AI/ML, and a forward-thinking mindset toward innovation in healthcare.

REQUIREMENTS

• 5+ years of experience in data engineering.
• Extensive experience in the design, build, and maintenance of data ETL pipelines.
• Extensive knowledge of coding in Python or Scala with a focus on data processing.
• Experience using Apache Spark (PySpark or Scala).
• Experience with AWS technology stack (S3, Glue, Athena, EMR, etc.).
• Experience with data and entity relationship modeling to support data warehouses and analytics solutions.
• Deep understanding of relational and non-relational databases (SQL/NOSQL).
• Comfortable working with unstructured and semi-structured data (Web scraping).
• Experience working in a professional software environment using source control (git), an issue tracker (JIRA, Confluence, etc.), continuous integration, code reviews, and agile development process (Scrum/Lean).
• Basic data privacy and security principles.
• Interest and/or experience in AI/ML applications, including support for model development or deployment workflows.
• Proactive mindset around exploring emerging technologies in AI and data science to drive innovation.

• Collaborate with Data Scientists and Business Intelligence Analysts to ensure efficient and effective data processing and analysis.
• Optimize data infrastructure and processes to ensure optimal performance and scalability.
• Develop and maintain data documentation and data lineage.
• Stay current with emerging technologies and industry trends related to data engineering.