Top Senior Level Data & Analytics Jobs in Miami, FL

Lead data engineer role at Wipfli focusing on the continuous advancement of a modern data lakehouse. Responsible for translating requirements into effective data models, developing in data technologies, managing project life cycle activities, achieving/maintaining proficiency in required skills, and collaborating with team members.

Headway is seeking a Senior Data Engineer to help create access to affordable, quality mental healthcare across the United States. This role involves collaborating on data infrastructure design, partnering with product/engineering for best practices, creating data pipelines for business insights, and building real-time data pipelines in coordination with the Data Science team.

As the Director of Data Science & AI, you will lead a team to develop AI and ML solutions that align with the business strategy. Responsibilities include overseeing the ML workflow, collaborating with Product and Engineering, and managing a high-performance team to drive customer-centric analysis and strategic initiatives.

The Customer Success Analytics Analyst will analyze customer data, build reports and dashboards, develop customer health scores, perform segmentation analyses, and evaluate the effectiveness of customer success programs while collaborating with cross-functional teams.

The Senior People Technology Analyst will be responsible for the implementation and maintenance of Workday and other People Systems. They will manage system configurations, provide end-user support, analyze data, and conduct training sessions while continuously seeking opportunities for process improvement.

The Senior Data Engineer at Spokeo will develop and optimize data technologies, build automation pipelines, collaborate with stakeholders to create data products, monitor performance, and follow data governance best practices.

The Sr. Manager will lead R&D teams to develop driver behavior detection algorithms from smartphone sensor data for Arity. Responsibilities include setting a vision for machine learning applications, aligning with product and engineering teams, and managing diverse data science teams while ensuring successful model deployment and strategic development.