Interested in this Data Engineer role at Boston Scientific?
Apply Now →Skills & Technologies
About This Role
Additional Location(s): US-MA-Marlborough; US-MN-Arden Hills
Diversity - Innovation - Caring - Global Collaboration - Winning Spirit - High Performance
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.
About the role:
Boston Scientific was recognized by Forbes as one of the Best Workplaces for Engineers in 2026, reflecting a culture where engineers do meaningful work. 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.
We are seeking an AI Data Engineer III to join our AI Engineering team and design, build and operate the data foundations that power enterprise AI products. In this hands-on role, you will develop scalable data platforms and pipelines for retrieval-augmented generation (RAG), enterprise search, document intelligence and knowledge-driven applications.
The incumbent will own complex data engineering components from design through production, including unstructured content processing, search and vector indexing, large language model (LLM)-based enrichment, graph data and application-facing APIs. You will partner with AI engineers, solution architects, software engineers, data scientists and business stakeholders to deliver secure, observable and cost-effective solutions in a regulated enterprise environment.
Work model, sponsorship, relocation:
At Boston Scientific, we value collaboration and synergy. This role follows a hybrid work model requiring employees to be in our local office Marlborough, MA or Arden Hills, MN 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.
Your responsibilities will include:
- Design, build and operate scalable AI data platforms supporting RAG, enterprise search, document intelligence, knowledge graphs and knowledge-driven applications.
- Develop production-grade pipelines to ingest, parse, normalize, chunk and enrich structured and unstructured enterprise content, including technical and clinical documents.
- Optimize document processing using language models and cloud services for classification, entity and relationship extraction, summarization, metadata generation, quality validation and traceability.
- Implement and tune retrieval strategies using embeddings, vector databases, keyword search, metadata filtering, hybrid search and reranking to improve relevance, grounding and response quality.
- Evaluate and support the migration and optimization of enterprise search indexes and vector data stores, including schema design, indexing strategies, performance testing, data refresh patterns and operational controls.
- Build and expand LLM-based solutions on Amazon Web Services (AWS) Bedrock, including prompt and model integration patterns, guardrails, evaluation and production monitoring.
- Develop and maintain RAG pipelines that connect governed enterprise content to conversational and agentic experiences.
- Design graph data models and pipelines representing products, documents, requirements, concepts and relationships, and integrate graph retrieval into search and AI workflows.
- Contribute to end-to-end chatbot delivery, including content ingestion, retrieval, service integration, conversation orchestration, evaluation, telemetry and production support.
- Create secure and reliable APIs, services and integration patterns that expose derived data and AI capabilities to applications, agents and enterprise systems.
- Implement automated testing, data quality controls and observability for pipelines and services, and troubleshoot complex production issues.
- Improve solution performance, reliability, scalability and cost efficiency through measurement, experimentation, capacity planning and engineering automation.
- Apply security-by-design, privacy-by-design and responsible AI practices, partnering with cybersecurity, legal, privacy, quality and platform teams to meet enterprise and regulated-environment requirements.
- Participate in design and code reviews, define reusable engineering patterns and standards, document technical decisions and mentor junior engineers.
- Collaborate with business and technical stakeholders to translate use-case needs into maintainable technical solutions and measurable outcomes.
Required qualifications:
- Bachelor's degree in computer science, software engineering, data engineering, information systems or a related technical field, or equivalent practical experience.
- Minimum of 3 years' experience in data engineering, software engineering or AI/machine learning engineering, including experience delivering production data pipelines or services.
- Hands-on experience with Python and SQL and software engineering practices such as version control, automated testing, code review and continuous integration and delivery.
- Experience building cloud-based data pipelines and services using AWS and/or Microsoft Azure.
- Experience designing or consuming APIs and integrating data services with downstream applications.
- Understanding of data modeling, data quality, lineage, logging and observability.
- Proven ability to communicate technical concepts clearly, collaborate across disciplines and independently own engineering work through production and ongoing operations.
Preferred qualifications:
- Experience with AWS Bedrock and related AWS services for generative AI, document processing, enterprise search, orchestration and monitoring.
- Experience with enterprise search, RAG and vector databases, including embeddings, chunking, index design, hybrid retrieval, migration, tuning and retrieval evaluation.
- Experience with graph databases, knowledge graphs or graph-enhanced retrieval, as well as conversational AI or agentic frameworks such as LangGraph, Semantic Kernel or similar technologies.
- Experience evaluating and operationalizing generative AI solutions using relevance, groundedness, answer quality, latency, cost and safety metrics, with familiarity in infrastructure as code, containerization, orchestration, MLOps or LLMOps.
- Experience with product lifecycle management platforms such as Windchill, highly regulated industries such as health care, life sciences or medical devices, and mentoring engineers or contributing to reusable technical standards and platform capabilities.
Requisition ID: 635649
Minimum Salary: $82100
Maximum Salary: $156000
The anticipated compensation listed above and the value of core and optional employee benefits offered by Boston Scientific (BSC) – see www.bscbenefitsconnect.com—will vary based on actual location of the position and other pertinent factors considered in determining actual compensation for the role. Compensation will be commensurate with demonstrable level of experience and training, pertinent education including licensure and certifications, among other relevant business or organizational needs. At BSC, it is not typical for an individual to be hired near the bottom or top of the anticipated salary range listed above.
Compensation for non-exempt (hourly), non-sales roles may also include variable compensation from time to time (e.g., any overtime and shift differential) and annual bonus target (subject to plan eligibility and other requirements).
Compensation for exempt, non-sales roles may also include variable compensation, i.e., annual bonus target and long-term incentives (subject to plan eligibility and other requirements).
For MA positions: It is unlawful to require or administer a lie detector test for employment. Violators are subject to criminal penalties and civil liability.
Boston Scientific transforms lives through innovative medical technologies that improve the health of patients around the world. As a global medical technology leader for more than 45 years, we advance science for life by providing a broad range of high-performance solutions that address unmet patient needs and reduce the cost of healthcare. Our portfolio of devices and therapies helps physicians diagnose and treat complex cardiovascular, respiratory, digestive, oncological, neurological and urological diseases and conditions. Learn more at www.bostonscientific.com and follow us on LinkedIn.
Boston Scientific Corporation has been and will continue to be an equal opportunity employer. To ensure full implementation of its equal employment policy, the Company will continue to take steps to assure that recruitment, hiring, assignment, promotion, compensation, and all other personnel decisions are made and administered without regard to race, religion, color, national origin, citizenship, sex, sexual orientation, gender identity, gender expression, veteran status, age, mental or physical disability, genetic information or any other protected class.
Please be advised that certain US based positions, including without limitation field sales and service positions that call on hospitals and/or health care centers, require acceptable proof of COVID-19 vaccination status. Candidates will be notified during the interview and selection process if the role(s) for which they have applied require proof of vaccination as a condition of employment. Boston Scientific continues to evaluate its policies and protocols regarding the COVID-19 vaccine and will comply with all applicable state and federal law and healthcare credentialing requirements. As employees of the Company, you will be expected to meet the ongoing requirements for your roles, including any new requirements, should the Company’s policies or protocols change with regard to COVID-19 vaccination.
Salary Context
This $82K-$156K range is in the lower quartile for Data Engineer roles in our dataset (median: $140K across 33 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 9,079 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Boston Scientific, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $177,000 based on 180 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($119K) sits 33% below the category median. Disclosed range: $82K to $156K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Boston Scientific AI Hiring
Boston Scientific has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Data Engineer. Based in Arden Hills, MN, US. Compensation range: $113K - $169K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.