Top Remote Senior Level Data Engineer Jobs

As a Senior Machine Learning Engineer in the Banking team at Cash App, you will develop and maintain machine learning systems to combat fraud in banking products. Your responsibilities will include designing ML pipelines, creating training platforms for models, and applying best practices to enhance ML solutions.

As a Senior Machine Learning Engineer at TrueML, you'll architect and implement ML infrastructure, develop production-grade ML pipelines, and maintain systems for real-time and batch decision-making. You'll collaborate with data engineers and data scientists to scale algorithms and ensure robust deployment of machine learning models.

The Senior Machine Learning Engineer at Samsara will be responsible for developing ML solutions to enhance the safety, efficiency, and sustainability of physical operations. This remote position requires working closely with engineering teams to deliver core infrastructure and services.

In this role, the Marketing Data Engineer will architect scalable ETL/ELT pipelines, manage cloud-based data warehouses, and develop data integration solutions. Responsibilities include collaborating with various teams to support analytics, ensure data quality, and optimize performance across data workflows.

As a Senior Data Engineer, you will design and maintain data infrastructure for Square's Marketing team, focusing on web event data. Responsibilities include building data pipelines, translating requirements for ETL processes, analyzing data sources, monitoring pipeline performance, and improving data quality and efficiency. Your work will significantly influence business decisions and reporting accuracy.

As a Principal ML Infrastructure Engineer, you will define and drive the technical vision for Thumbtack's ML infrastructure, lead cross-functional initiatives, architect scalable ML systems, establish engineering standards, mentor teams, and align infrastructure capabilities with business objectives.

The Senior Director of Data will lead a multidisciplinary data organization focused on analytics, data infrastructure, and AI initiatives. Responsibilities include building data strategies, managing a team of data professionals, and ensuring effective use of customer data while partnering across the organization to optimize product features.