Senior Data Engineer

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At Boston Scientific, we’ll give you the opportunity to harness all that’s within you by working in teams of diverse and high-performing employees, tackling some of the most important health industry challenges. With access to the latest tools, information and training, we’ll help you in advancing your skills and career. Here, you’ll be supported in progressing – whatever your ambitions.

Boston Scientific was recognized as a Glassdoor Best Place to Work in 2026, ranking No. 15 on the Top 100 list, reflecting the culture our employees experience every day.

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 opportunitty 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.

At Boston Scientific, we value collaboration and synergy. This role follows an onsite work model requiring employees to be in our local office at least three days per week. Boston Scientific will not offer sponsorship or take over sponsorship of an employment visa for this position at this time. Relocation assistance is not available for this position at this time.

• 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, Git Lab, 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.

• 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…