Top Remote Senior Data Engineer Jobs in NYC, NY

As a Senior Data Engineer, you will develop and maintain scalable data platforms, architect high-volume data pipelines, improve system performance, ensure data privacy compliance, and collaborate with teams for operational efficiencies. You are expected to debug complex issues and share knowledge through documentation and mentoring.

As a Senior Data Engineer at Bombora, you will develop and optimize systems to process billions of interactions daily, identify intent signals, and lead engineering initiatives. Responsibilities include architecting ETL pipelines in Google Cloud Platform, developing microservices, and mentoring junior engineers while working within an Agile environment.

The Manager of Data Engineering will lead a team of six data engineers, focusing on technical leadership, mentoring, fostering team culture, and managing project timelines. This role involves collaborating with product management, driving the design of Canoe's data platform, and ensuring successful project execution.

As a Senior Data Engineer, you will design and improve data infrastructure, build data pipelines for various stakeholders, and collaborate closely with Data Science and Analytics teams to produce valuable business insights and support core products.

The role involves building computer vision models for 3D scans, researching state-of-the-art machine learning methods, working with product and research teams to improve product lines, and implementing best practices for MLOps pipelines, all while working with large datasets.

The role involves managing vendor relationships and data center strategies. Responsibilities include negotiating contracts, planning infrastructure growth, collaborating with various teams, and conducting vendor performance reviews. The ideal candidate has extensive experience in the data center industry and excels in multitasking and project management in a high-growth environment.

As a Data Engineer at Kustomer, you will enhance the data architecture and streamline data-ingestion processes, manage ETL processes, analyze data to extract insights, debug data issues, and provide production support for the data pipeline.