Top Senior Level Data Engineer Jobs in NYC, NY

As a Senior Machine Learning Engineer on the On-Device ML team at Grammarly, you will design and build ML models, propose new features, integrate models into user interfaces, and monitor deployments through A/B testing while collaborating with core product teams.

The Senior Architect will provide guidance on DataBricks, design data flow and processing pipelines, manage data architecture, assist clients with data management, and coach consultants. The role includes ensuring data integrity and coordinating with application owners for analytics integration.

As a Senior Data Engineer at Hinge, you will design, build, and optimize data pipelines, enhance data modeling techniques, and maintain analytics data warehouses. Your efforts will improve product services, ensuring data consistency and low latency while directly impacting user experience and product performance. You'll work cross-functionally to support data-driven decisions and participate in on-call rotations for system reliability.

As a Staff Engineer on the Data & Analytics team, you will lead high-visibility projects, provide technical leadership, mentor engineers, and drive architectural decisions for the platform. You will collaborate with product teams to enhance data management ecosystems and ensure system reliability and scalability for data ingestion and processing.

As a Machine Learning Engineer, you'll identify and integrate ML opportunities into the product, prototype models, collaborate with the Product Engineering team, and create data pipelines to support ML systems. You'll leverage your expertise in natural language processing and provide guidance to enable other teams to build ML-powered features.

As a Senior EDI Analyst, you will monitor and improve EDI systems that support procure-to-pay and order-to-cash processes, troubleshoot issues, and participate in cross-functional teams for new EDI document launches.

The Senior Engineer will be responsible for architecting and building scalable data processing systems to handle large data volumes, collaborating with teams to optimize system performance and costs, and engaging in the full software development lifecycle, including production support.