Senior Data Engineer (Customer Data Products)

NBME is seeking a Senior Data Engineer to modernize, expand, and optimize its data platform. The role builds scalable data solutions and pipelines supporting analytics, AI/ML, and business decision-making, while promoting AI-assisted engineering practices.

Responsibilities:

• Code, test, deploy, orchestrate, monitor, document, and troubleshoot cloud-based data engineering processes, feature stores, and vector databases in accordance with best practices and security standards throughout the development lifecycle
• Partner closely with data scientists, AI researchers, data and enterprise architects, and business stakeholders to identify, extract, clean, and format structured and unstructured data for AI/ML model training, fine-tuning, and feature extraction
• Lead evaluation, research, and experimentation efforts with batch and streaming data technologies, LLM data preparation frameworks, and MLOps tools to keep pace with industry innovation
• Act as a technical lead to showcase the capabilities of emerging AI and data technologies, enabling the widespread adoption of modern data techniques across the organization
• Significantly contribute to the definition and refinement of processes and procedures for the data engineering practice
• Educate and develop ETL developers on data engineering cloud-bases initiatives to enable transition to data engineer and practice
• Assures the integrity and accuracy of the corporate data, with particular attention to data security
• Responsible for ensuring high data quality for Data Services, Analytics and Master Data Management
• Helps coordinate technical solutions, takes responsibility for designs, development, testing and delivery of solutions
• Build automated, scalable, test-driven data pipelines
• Utilize software development practices such as version control via Git, CI/CD, and release management to enhance existing CI/CD pipelines in AWS
• Collaborate with Data Engineers, DevOps engineers and architects on improvement opportunities for DataOps tools and frameworks

Requirements:

• Bachelor's Degree
• At least 7 years of experience in application development (Internship experience does not apply)
• At least 4 years of experience in big data technologies
• At least 4 years' experience with cloud computing using AWS
• 4+ years of experience in application development including Python, SQL, Scala, or Java
• 4+ years' experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, MySQL etc.)
• 4+ year experience working on real-time data and streaming applications
• 4+ years of experience with NoSQL implementation (Mongo, Cassandra)
• 4+ years of data warehousing experience (Redshift)
• 6+ years of experience with UNIX/Linux including basic commands and shell scripting
• 7+ years of experience with Agile engineering practices
• 7+ years of experience with SQL optimization
• 4+ years of experience with PySpark
• 3+ year of experience with process orchestration including AirFlow, KubeFlow, AWS step functions, or Luigi
• Proven experience implementing Generative AI, LLM data preparation pipelines, and Vector Databases (e.g., Pinecone, Milvus, pgvector)
• Strong experience building and maintaining Feature Stores for machine learning models
• Experience building highly scalable, secure, and production-ready APIs and Data-as-a-Service (DaaS) platforms
• AWS Certified Data Engineer or AWS Certified Machine Learning – Specialty certifications
• 3+ year of experience with Machine Learning
• Experience with building a Data-as-a-service platform
• Experience with building APIs