Bluespine-Senior Data Engineer, Analytics & Data Infrastructure

Description About Bluespine Bluespine was established by professionals in the industry and serial founders to support self-funded employers in reducing healthcare expenses. Through innovative data analysis, Bluespine helps minimize unnecessary costs without altering employee benefits or requiring changes in behavior. We are seeking a Senior Data Engineer, Analytics & Data Infrastructure to join our team to help build and evolve our data infrastructure. The ideal candidate combines strong hands-on data engineering skills with ownership of both analytical data assets and core data infrastructure. This role is responsible for designing robust data architectures, building reliable and scalable data pipelines, supporting analytics and AI/ML workflows, and ensuring that high-quality data is available for business, product, and data science use cases. Responsibilities Take full ownership of specific data pipeline components, from development and testing to deployment, monitoring, and maintenance. Design, build, and optimize scalable data pipelines for processing, transformation, and ingestion workflows. Leverage modern AI tools and frameworks to accelerate data engineering workflows and improve development velocity. Collaborate closely with data scientists to architect and implement pipelines for training and inference use cases, ensuring high-quality and reliable model inputs. Build pipelines that support AI/ML workflows, including embeddings, vector data, and preprocessing of structured and unstructured data. Implement proactive monitoring, data quality checks, and automated alerting to ensure the reliability, accuracy, and availability of data assets. Contribute to data modeling, schema design, and performance optimization across analytical data stores. Requirements 3+ years of experience in Data Engineering. Formal education in Computer Science or a related technical field. Expert-level Python proficiency, with the ability to write clean, maintainable, and efficient code for data processing. Experience with modern analytical data stores such as BigQuery, ClickHouse, Snowflake, or similar. Proven experience building data pipelines Strong cloud-native experience with GCP or AWS. Proficiency with modern orchestration tools such as Airflow, Dagster, or Prefect. Strong understanding of data modeling, schema design, performance optimization, and building reliable, scalable datasets.