Principal Data Engineer - Snowflake & AI-Driven Pipelines

New Balance Athletics, Inc.; Boston (MA); Ibrido; USD 139.000 - 208.000; Tempo pieno

Vantaggi offerti da questo lavoro

Medical insurance

Dental & Vision

401K

Tuition reimbursement

Learning & development

Lifestyle reimbursement

Parental leave

Descrizione del lavoro

New Balance Athletics, Inc. is seeking a Principal Data Engineer II to design and optimize enterprise data infrastructure using Snowflake, Snowpark, and Azure data services.

You will lead cross‑functional teams to deliver scalable data products and resilient pipelines across cloud and on‑prem sources. You will mentor engineers, advance CI/CD practices, and apply practical AI techniques to automate data quality, source‑to‑target reconciliation, and pipeline testing within a hybrid North American

Competenze

• 11–13 years of experience in data warehouse and data engineering.
• Proven ability to lead architecture and develop complex pipelines.
• Strong SQL, Python, Snowflake proficiency; cloud data platform design.

Mansioni

• Lead architecture and development of data pipelines across on‑prem, cloud, batch, streaming, and event sources.
• Define reusable data products and governance for multiple business domains.
• Apply AI/Large Language Model techniques to automate quality and reconciliation.
• Mentor engineers and drive CI/CD, testing, and operational excellence.

Conoscenze

SQL

Python

Snowflake

Data pipelines

CI/CD

GitHub

dbt

Azure Data Factory

Azure Data Lake

LLM/AI in data

Formazione

Bachelor's degree in Computer Science

Strumenti

Snowpark

GitHub Actions

Azure Data Factory

dbt

Descrizione del lavoro

New Balance Athletics, Inc. is seeking a Principal Data Engineer II to design and optimize enterprise data infrastructure using Snowflake, Snowpark, and Azure data services.

You will lead cross‑functional teams to deliver scalable data products and resilient pipelines across cloud and on‑prem sources. You will mentor engineers, advance CI/CD practices, and apply practical AI techniques to automate data quality, source‑to‑target reconciliation, and pipeline testing within a hybrid North American