Top Senior Level Data & Analytics Jobs

The Data Scientist - Architect will design, develop, test, and support scalable machine learning solutions using Azure tools. Responsibilities include leading technical discussions with clients, assisting with pre-sales activities, elevating team talent through coaching, and identifying opportunities for Azure solution architecture in client accounts.

The Senior Software Engineer will create and maintain data processing pipeline architecture, build infrastructure for data extraction and transformation, analyze complex data sets, and support stakeholders with data-related issues, primarily using Python, SQL, and AWS technologies.

The Senior Data Scientist will lead data science projects using machine learning and statistical techniques, identify opportunities for data science, measure business impact, and mentor junior team members.

The Program Analyst will support the smooth delivery of Electric Vehicle programs by managing data, conducting complex analyses, producing reports, ensuring compliance, and leading teams in meetings. The role involves special projects and requires a strong grasp on operational procedures.

Deliver scalable, performant ETL processes in multi-tenant cloud infrastructures, focusing on healthcare data. Design and implement enterprise data schemas, clean and transform data sets, and operate ETL software components. Collaborate with team members and clients to achieve scalability and high performance.

The Senior Data Engineer at Bluesight will build and maintain robust data architectures and infrastructure, develop data governance practices, support data products, and collaborate with engineering and external customers to enhance medication workflows. The role involves using various data orchestration tools and programming languages, while ensuring effective communication about data implementations across teams.

The Data Architect will be responsible for designing, implementing, and optimizing data solutions to support business needs. Responsibilities include evolving data infrastructure, architecting data systems, maintaining data pipelines, establishing data governance, and mentoring team members.