Top Tech Jobs & Startup Jobs

The Senior Analytics Engineer at ShyftLabs will design and implement cloud-based data solutions, develop data models for KPI capture, and contribute to building software pipelines. The role requires close collaboration with stakeholders and a focus on innovative solutions to meet client objectives. The successful candidate should have proficiency in machine learning, experience in analytics, and strong interpersonal skills.

Join ShyftLabs as an Apache Druid Engineer to work on cutting-edge data projects with a focus on real-time analytics solutions and data infrastructure optimization. Collaborate with cross-functional teams to deliver high-quality data products and contribute to the growth of the business.

As a Backend Developer, you will design and maintain scalable applications using TypeScript, develop APIs (RESTful and GraphQL), ensure code quality, troubleshoot existing applications, and collaborate with cross-functional teams. You will also utilize AWS services for application management and stay updated on emerging technologies.

The Architect/Lead Developer will design scalable software solutions, lead hands-on development, provide architectural support, mentor teams, create GraphQL APIs, integrate AWS services, ensure code quality, and collaborate across teams.

The Adobe Target Developer will deliver complex solutions, execute marketing tests, personalize experiences, and develop analytic dashboards for retail clients. Responsibilities include configuring Adobe Target, troubleshooting issues, and working with cross-functional teams to ensure successful execution of testing strategies.

As a Data Scientist at ShyftLabs, you will analyze datasets, develop simulations, and design experiments to provide actionable insights for improving a complex ML bidding system. Your role involves collaborating with stakeholders and utilizing statistical techniques to drive data-driven decisions.

The Data Scientist will research and develop generative AI models, collaborate with teams to implement AI-driven solutions, train and optimize models, ensure data quality, and integrate AI/ML models into production applications while maintaining documentation and facilitating communication.