Top Remote Artificial Intelligence Data Engineering Jobs in New York City, NY

The role involves managing data pipelines for AI and quantitative research, focusing on L3 market data architecture and integrity, API development, and collaboration with researchers.

The AWS Data Engineer will design, build, and optimize data pipelines on AWS, ensuring performance and data quality for analytics. Responsibilities include developing ETL processes, collaborating with teams, and monitoring data workflows.

As an Analytics Engineer, you will transform raw data into reliable insights, build data models, and conduct analysis to support product and business decisions.

The Senior Data Engineer will design and maintain scalable data pipelines, lead medium-sized platform projects, and ensure reliability of data systems through monitoring and optimization. Responsibilities include working with cross-functional teams to improve data workflows and participate in incident response.

The Founding Platform/Infra Engineer will design and manage infrastructure, improve system observability and CI/CD processes, enhance security, and boost developer efficiency at Coverbase.

As a Staff Product Analytics Engineer, you will build and maintain data infrastructure, collaborate across teams to enhance product data use, and develop tools for data self-service.

The Analytics Engineer will enhance BI infrastructure, collaborate with Data Engineering, and deliver analytics-ready data. Responsibilities include managing Snowflake, dbt projects, and CI/CD workflows, optimizing data structures, and supporting BI initiatives.

As an Associate Technical Architect, you will solve big data problems and develop solutions for migration, storage, and processing, focusing on Databricks and data engineering.

Design, build, and maintain scalable data pipelines and tooling to support AI and analytics. Manage datastores (MySQL, Redshift, OpenSearch), implement AWS-based ETL/ELT solutions, collaborate with data science and product teams, and ensure data quality, automation, and production reliability.

The Engagement Manager will oversee the data modernization lifecycle, coordinating with partners to implement modern data platforms and manage multiple concurrent engagements effectively.