Remote Data Jobs

Shape the backbone of our data strategy—designing scalable pipelines, championing governance, and enabling AI-ready infrastructure. Lead cross-functional projects that fuel analytics, automation, and decision-making across the Playlist ecosystem. Your work will directly empower our teams to create personalized, real-world experiences for millions of users.

You will own the design, development, and maintenance of TextNow's data platform, enabling effective data-informed decisions as an experienced Data Engineer with hands-on experience designing and developing data platforms. You will be part of cross-functional efforts to build scalable and reliable frameworks that support all TextNow's business and data products. In this role, you can interact with different functional areas within the business and influence decision-making in a fast-growing mobile communications start-up.

As the Lead Data Engineer at Yobi, you will be at the center of how data moves, scales, and powers our Behavioral AI products. You will be working on various virtual teams and projects, with an eye towards schema modeling, orchestration, abstractions, workload efficiency, reliability/observability, and so on. This is a hands-on engineering role.

This engineering role is within the Strategy & Operations team, building AI-first solutions to ensure every facet of Alignable is AI-powered and data-driven. You will own projects that change how the entire company runs, like customer support automation, sales agents automation, data pipelines, and internal AI tools. You'll work closely with executives and business leaders, impacting the whole company with your work on critical projects.

The Director, Data Engineering will lead Data Platform initiatives, defining the strategy, architecture, and execution of our end-to-end data ecosystem, encompassing data ingestion pipeline, operational data stores, our evolving data lakehouse, and robust data APIs. This role requires deep technical expertise and a strong understanding of how data can drive business value.

Seeking a talented and motivated Data Architect to join our team in a project supporting the consolidation of data, systems and corporate processes on a global scale. You will lead the migration of data from complex systems to a unified architecture. Your role will be to ensure that the migrated data systems meet the specific needs of the business, providing a smooth and efficient transition.

As a Machine Learning Data Scientist on Extend’s Risk & Fraud Machine Learning Team, you will develop and deploy cutting-edge machine learning models to detect and prevent fraud, enhance decision-making, and drive business value. You’ll work closely with product, engineering, and operations teams to build scalable, production-ready machine learning applications that support Extend’s post-purchase products.

As a Manager Data Scientist, you'll collaborate closely with business leaders to address our clients' diverse business challenges. You will lead and mentor junior data scientists, helping them achieve their goals and ensuring the team's success. This role requires strong business acumen to understand and solve complex problems, ensuring the successful implementation of data-driven solutions.

Be part of the Data Engineering team while supporting the Data Science team, where your role involves delivering clean, reliable, and automated data processes that support financial decision-making by building and optimizing data pipelines from multiple source systems into Azure Databricks, ensuring accuracy, scalability, and alignment with enterprise architecture standards.

Responsible for developing AI and ML solutions to drive improved business performance and efficiency across domains, you will lead end-to-end AI/ML projects from understanding business needs to data preparation, model development, solution deployment, and post-production monitoring. You will dissect and solve complex business problems using analytical and quantitative approaches to identify patterns and correlations in large complex data sets.