Principal Data Engineer

New Balance•Boston, MA

1d•Hybrid

About The Position

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.

Requirements

Bachelor's degree in Computer Science, Management Information Systems, or a related field, or equivalent professional experience.
11-13 years of experience in data warehouse and data engineering development, including building, managing, and optimizing enterprise data pipelines in cloud computing environments.
Proven ability to lead the architecture and development of complex data pipelines and integrations across multiple source systems, with a thorough understanding of the software development life cycle.
Strong understanding of data architecture, data modeling, data quality, observability, and secure engineering practices.
Expert proficiency in SQL and Python, with strong analytical, troubleshooting, and problem-solving skills.
Deep experience with Snowflake data warehousing and data engineering capabilities, including performance optimization, cost-conscious design, and development using SQL, Python, and Snowpark.
Ability to translate business requirements into technical specifications, including data sources, integration methods, transformations, data models, validation controls, and operational requirements.
Experience designing and delivering reusable data products across multiple business domains, including data modeling, pipeline development, quality controls, documentation, and operational support.
Experience with Azure data integration services, including Azure Data Factory and Azure Data Lake Storage or Azure Blob Storage.
Experience with data transformation and software delivery practices using tools such as dbt, GitHub, GitHub Actions, automated testing, and CI/CD.
Experience using AI coding agents to accelerate pipeline development and testing and applying LLM-powered techniques to automate data-quality validation or reconciliation in ELT pipelines.
Demonstrates technical ability, thoroughness, and accuracy in all assignments, with sufficient understanding of technical databases and business applications to analyze issues and develop results-oriented conclusions.
Excellent written and verbal communication skills with staff, managers, external customers, and vendors; ability to explain technical concepts to technical and non-technical audiences, influence cross-functional decisions, and proactively raise risks and issues.
Demonstrated sense of urgency, initiative, and adaptability to changing priorities; sound judgment, attention to detail, and required confidentiality; effective influencing and negotiation skills; and the ability to mentor engineers.

Responsibilities

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 stay current with emerging data engineering and platform capabilities.