Principal Data & Machine Learning Engineer Full-time

Job Summary

NetApp is seeking a capable Principal Data & Machine Learning Engineer to join Data Services organization. The overarching vision of this organization is to empower organizations to effectively govern their data estate and build cyber-resiliency while accelerating their digital transformation journey. To get to this vision, we will embark on an AI-first approach to build and deliver world-class suite of data services. As a key technical leader in this initiative, the Principal Data & Machine Learning Engineer will be responsible for architecting and building data and ML pipelines across the key pillars of data protection, privacy and compliance. The ideal candidate, while overseeing the technical work of 1-3 data and machine learning engineers, will also possess deep subject matter expertise in the areas of AI/ML infrastructure and platforms with a demonstrated history of shipping impactful at-scale AI/ML solutions into production.

Job Requirements
• Develop and maintain scalable data pipelines for various AI/ML-driven data services, which include ensuring reliable data ingestion, transformation, integration from various sources and maintaining ML feature and deployment pipelines.
• Ensure that data architectures and infrastructure can scale seamlessly as the data volume and complexity grow for NetApp’s data services.
• Build and maintain self-service data exploration and analysis platforms to derive insights from their respective data sources.
• Provide guidance and support to data analysts, engineers, scientists, and other stakeholders in leveraging the available data resources effectively.
• Oversee ML design reviews, create best practices and playbooks for end-to-end ML systems in production.
• Work with a great deal of autonomy and be the technical thought leader in creating a forward-looking vision with clear direction.
• Effectively communicate complex technical artifacts to both technical (engineers & scientists) and non-technical audiences
• Work closely with cross-functional teams including business stakeholders to innovate and unlock new use-cases for our customers that is driven through data intelligence.
• Participate in cross-functional meetings, workshops, and planning sessions to ensure data engineering activities support the overall objectives across data services and platform initiatives.
• Coaching and leadership for data professionals and the broader cross-functional team, helping influence and develop their skills and capabilities by fostering a culture of innovation and continuous learning.
• Have a strong customer focus and build data products that delight our users.
• Represent NetApp as a leader and ambassador in the machine learning community, building relationships with external partners and promoting the company’s product capabilities in industry/academic conferences.

Education
• Master’s in computer science, Engineering, Applied Mathematics/Statistics/Data Science, or any quantitative field.
• 10+ years of relevant industry experience as a data and machine learning engineer, with a strong track record of shipping successful AI/ML products at scale.
• 3+ years’ experience building data/feature pipelines and deployments for ML models across various domains in AI/ML (NLP preferred).
• High-level understanding of supervised and unsupervised machine learning algorithms and 5+ years of experience shipping them in production.
• Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders at all levels of the organization.

Preferred Qualifications:
• PhD in Computer Science, Engineering, Applied Mathematics/Statistics/Data Science, or any quantitative field.
• 3+ years’ experience in technically leading a team of data and machine learning engineers.
• Experience building pipelines for end-to-end AI/ML model deployment in the areas of security and/or privacy and/or compliance.
• Ability to represent the company as a thought leader in the data engineering community.
• High level understanding of deep learning approaches, preferably in the area of Natural Language Processing