Senior Data Engineer- AI & ML Enablement

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Design, build, and maintain robust ETL/ELT pipelines supporting large, complex datasets with evolving schemas
Support embedding generation and vector-based data pipelines for AI and GenAI use cases
Develop scalable batch and streaming data pipelines using cloud-based data platforms (e.g., Snowflake or Databricks)
Create and evolve semantic data models that transform raw data into analytics-ready, trustworthy datasets
Build data preprocessing, validation, and quality-assurance tooling to ensure reliability and correctness
Design pipelines that support both offline model training and low-latency inference workloads
Drive the design of modern data architectures that balance scalability, performance, and maintainability
Implement data quality checks, lineage, and monitoring for enterprise-scale datasets
Work closely with product, analytics, AI/ML, IT, and business teams to understand data needs and enable data-driven initiatives
Interface with stakeholders across manufacturing, quality, post-market surveillance, commercial, and clinical domains
Provide technical leadership and mentorship to other data engineers, promoting best practices and continuous improvement

Bachelor’s Degree or higher in Computer Science, Mathematics, engineering or a related technical field AND 6-8 years of job related experience OR High School Diploma/GED from AND 10 years of the same job-related experience

Strong experience with SQL, relational and NoSQL databases
Hands-on experience building and maintaining large-scale data pipelines in cloud environments (Azure or AWS)
Experience with Databricks or Snowflake and distributed data processing frameworks (e.g., Spark)
Familiarity with feature stores, vector databases, or model-adjacent data systems
Experience managing data quality, validation, and lineage for large, complex datasets
Proficiency in Python and experience with data-frame libraries (e.g., Pandas, Polars)
Experience with ETL/workflow orchestration tools (e.g., Databricks Workflows, Azure Data Factory, AWS Glue)
Familiarity with common data formats (CSV, JSON, XML, Parquet, etc.)
Strong communication skills with the ability to explain technical concepts to non-technical stakeholders
Professional proficiency in English, with regular collaboration across global team

Experience in healthcare, medical device, life sciences, or manufacturing environments
Familiarity with Business Intelligence tools and analytics platforms
Experience supporting AI/ML use cases through high-quality, well-modeled data
Knowledge of data privacy, security, and compliance best practices

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