Senior Data Engineer

Senior Data Engineer Boston Scientific's Active Implantable Systems (AIS) R&D organization is seeking a Senior Data Engineer to join our AI Transformation core team and design and build the data pipelines that power our AI-enabled engineering workflows. This is an exciting opportunity to join a growing team that will enable the re-imaging of how work gets done across our R&D engineering functions. You will own data curation, indexing quality and pipeline reliability, ensuring that engineering knowledge — requirements, design history, test data, quality records and more — is current, trusted and AI-accessible. You will work closely with the AI Solutions Architect and AI Solutions Delivery roles to ensure engineering data sources are structured and governed in a way that supports retrieval-augmented generation, evaluation and agentic workflows across AIS R&D, while meeting the data quality, lineage and compliance standards required in a regulated product development environment. Your responsibilities will include: Design and build data pipelines that ingest, curate and index engineering data sources to support AI-enabled workflows. Own data curation, indexing quality and pipeline reliability across all in-scope engineering data sources. Ensure engineering knowledge — requirements, design history files, test data, quality records and related artifacts — is current, trusted and AI-accessible. Define and enforce data quality checks, schemas and governance practices for operational AI systems. Partner with the AI Solutions Architect on the context layer, versioned prompt/agent specification library and retrieval architecture. Partner with the AI Solutions Delivery role and Enterprise AI/IT on integration patterns across engineering systems (e.g., Windchill, Jira, GitLab, AWS). Scale pipeline reliability and indexing coverage as additional workflows move from pilot to production. Support evaluation-harness data needs, including test data preparation and data-quality regression checks. Contribute to a culture of ownership, experimentation and data-driven decision-making. Qualifications Required qualifications: Bachelor's or master's degree in computer science, data engineering, data science or a related technical field. Minimum of 7 years' experience designing, building and operating production data pipelines. Strong experience with data curation, indexing and data quality frameworks at enterprise scale. Experience with modern cloud data platforms (e.g., AWS, Snowflake, Databricks) and ETL/ELT frameworks. Advanced SQL skills and experience optimizing large-scale analytical or operational datasets. Experience implementing data governance practices, including metadata management, lineage and data quality controls. Experience working in a regulated, enterprise-scale environment with security, compliance and quality requirements. Preferred qualifications Experience supporting retrieval-augmented generation (RAG), vector databases, embeddings or semantic search. Experience building data pipelines that support machine learning, LLM or agentic AI workflows. Experience with engineering data sources such as PLM (e.g., Windchill), requirements management or version control systems. Familiarity with MLOps/LLMOps practices, including data versioning and pipeline monitoring. Experience in health care, life sciences, medical devices or another highly regulated industry. Intracept by Boston Scientific