Top Senior Level Data Engineer Jobs in Miami, FL

Lead the design and development of data architecture supporting data modeling, integration, and delivery standards. Collaborate with cross-functional teams to optimize data lakes, enhance data strategy, and ensure robust analytics usage.

The Senior Data Science Consultant at DesignMind will lead client engagements, build machine learning systems, and mentor teams while communicating with stakeholders. Responsibilities include data modeling, statistical analysis, and delivering insights through documentation and presentations.

The Data and Integrations Consultant will lead the delivery of integration services by providing guidance and support to customers, ensuring seamless data flow between their CRM and ERP systems and AcuityMD's platform. Responsibilities include troubleshooting, maintaining integrations, and collaborating with internal teams to develop tailored solutions while identifying opportunities for upselling consulting services.

As a Staff Machine Learning Engineer, you will enhance Lime’s demand forecasting and positioning algorithms, execute a multi-year ML strategy, and work collaboratively across teams. Your role includes coaching engineers and data scientists while tackling complex ML challenges.

The Senior Data Engineer at OneSignal will design, build, and maintain data systems to ensure reliable data access across various business functions including sales, marketing, and operations. Responsibilities include connecting data sources, improving data flows, creating ETLs, and implementing data validation processes.

The Principal Data Engineer will design and implement end-to-end data workflows for ML modeling, improve model performance, and enhance data processing frameworks. Responsibilities include creating standards and best practices, ensuring data accuracy, and collaborating with data scientists to meet data needs.

The Lead Data Engineer will manage and mentor a data engineering team to deliver data solutions for clients. Responsibilities include overseeing data pipeline development, ensuring client integrations, applying data best practices, and managing project timelines while contributing hands-on to technical decision-making and code reviews.