Principal Data Engineer

Who We Are:

Since 1906, New Balance has empowered people through sport and craftsmanship to create positive change in communities around the world. We innovate fearlessly, guided by our core values and driven by the belief that conventions were meant to be challenged. We foster a culture in which every associate feels welcomed and respected, where leaders and creatives are inspired to shape the world of tomorrow by taking bold action today.

New Balance is seeking an experienced Principal Data Engineer II with deep expertise in Snowflake to join our Global Data & Analytics team, where we harness data to deliver actionable insights and drive innovation across the business. As Principal Data Engineer II, you’ll play a crucial role in designing, developing, and enhancing our enterprise data infrastructure, partnering with data consumers, platform teams, and cross-functional stakeholders to translate business requirements into scalable data solutions and reusable data products across business domains.

You’ll also build and optimize the analytical pipelines and multi-source integrations that make trusted data available across the organization. If you enjoy solving complex data challenges, building resilient data platforms, and advancing the practical use of AI in data engineering, we’d love to have you on the team.

Lead the architecture, design, and development of reliable data pipelines that acquire, ingest, transform, and deliver data across on-premises, cloud, batch, streaming, and event-driven sources, and define reusable architecture patterns and engineering standards for the platform. Unify batch, streaming, and event-driven processing within a cohesive, monitored, and supportable cloud data architecture, with appropriate alerting, recovery, and operational controls. Partner with business and IT stakeholders and data consumers to define requirements, identify source systems and integration methods, and model business and system processes through use case scenarios, workflow diagrams, and data models aligned with the enterprise data architecture.

Design and deliver reusable, well-governed data products across business domains, partnering with data owners and consumers to define data requirements, quality standards, documentation, ownership, and operational expectations. Lead technical design reviews and guide engineering decisions related to scalability, reliability, security, maintainability, performance, and cost optimization. Use practical AI capabilities, including AI coding agents (e.g., Snowflake CoCo) and LLM-powered techniques (e.g., Snowflake Cortex AI Functions), to improve pipeline development, testing, and operations and to automate data-quality validation and source-to-target reconciliation, with appropriate security, governance, human review, and production controls.

Debug, profile, and optimize integrations and ETL/ELT processes for performance, reliability, and efficient platform resource consumption. Identify and implement process improvements by automating manual activities, optimizing data delivery, strengthening CI/CD and testing practices, and improving the scalability and supportability of the data platform. Document application and data architecture, engineering standards, operating procedures, and technology decisions; create and maintain operational runbooks;

and recommend practical improvements and alternatives. Address production issues promptly, provide on-call support as needed, keep stakeholders informed, and identify delivery risks or obstacles with sufficient lead time. Foster a collaborative team environment through open communication and technical mentorship, promote knowledge sharing and succession planning, and…