New Balance is seeking a Principal Data Engineer II for the Global Data & Analytics team. In this role, you will help strengthen enterprise data infrastructure by leading reliable, governed data pipelines and reusable data products. You will also apply practical AI capabilities to accelerate pipeline development, testing, and automated data-quality validation, with security, governance, and human review built into production workflows.
This hybrid position is based in Brighton, MA. New Balance offers a comprehensive benefits package, learning and tuition support, and flexibility through a North American hybrid model plus a Work from Anywhere (WFA) option.
Responsibilities
- Lead 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, while defining reusable architecture patterns and engineering standards.
- Unify batch, streaming, and event-driven processing into a cohesive cloud data architecture with monitoring, alerting, recovery, and operational controls.
- Partner with business and IT stakeholders to define requirements, identify source systems and integration methods, and model business and system processes using use case scenarios, workflow diagrams, and aligned data models.
- Design and deliver reusable, well-governed data products across business domains, collaborating with data owners and consumers on data requirements, quality standards, documentation, ownership, and operational expectations.
- Lead technical design reviews and guide engineering decisions across scalability, reliability, security, maintainability, performance, and cost optimization.
- Use AI capabilities, including Snowflake CoCo and Snowflake Cortex AI Functions, to improve pipeline development, testing, and operations, including automation of data-quality validation and source-to-target reconciliation with appropriate production controls.
- Debug and optimize integrations and ETL/ELT processes for performance, reliability, and efficient platform resource consumption.
- Identify process improvements by automating manual work, optimizing data delivery, and strengthening CI/CD and testing practices to improve scalability and supportability.
- Document application and data architecture, engineering standards, operating procedures, and technology decisions, maintain operational runbooks, and recommend practical improvements and alternatives.
- Respond to production issues, provide on-call support as needed, communicate delivery risks with sufficient lead time, and remove obstacles.
- Support a collaborative environment through open communication, technical mentorship, knowledge sharing, succession planning, and staying current with emerging data engineering and platform capabilities.
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 environments.
- Proven ability to lead 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 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 sources, integration methods, transformations, data models, validation controls, and operational requirements.
- Experience designing and delivering reusable data products across multiple business domains, including 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.
- Excellent written and verbal communication skills; ability to explain technical concepts to technical and non-technical audiences and influence cross-functional decisions.
- Demonstrated urgency, initiative, adaptability, sound judgment, attention to detail, and ability to mentor engineers.
Technologies
Snowflake, SQL, Python, Snowpark, Snowflake CoCo, Snowflake Cortex AI Functions, LLM, ETL, ELT, Azure Data Factory, Azure Data Lake Storage, Azure Blob Storage, dbt, GitHub, GitHub Actions, CI/CD, automated testing, streaming, event-driven.
Compensation & Schedule
- Annual base pay range: $138,500 - $207,500 (Boston, MA headquarters pay range).
- Hybrid schedule: in office three days per week (Tuesday, Wednesday, Thursday).
- Work from Anywhere (WFA): four weeks per calendar year.
Benefits
- Comprehensive traditional benefits package including three options for medical insurance, plus dental, vision, life insurance, and 401K.
- Online learning and development courses; tuition reimbursement.
- $100 monthly student loan support.
- Various mentorship programs.
- Yearly $1,000 lifestyle reimbursement, plus 4 weeks of vacations and 12 holidays.
- Generous parental leave.
Regular associate benefits also include yearly lifestyle reimbursement, vacations, holidays, and generous parental leave. Depending on associate status (temporary or part time), additional benefits may include medical, dental/vision coverage, 401k, short term disability, and associate discounts.
