Top Remote Analysis Reporting Jobs in Orlando, FL

Build and maintain scalable data pipelines and Python automations for ingesting, processing, and enriching data. Integrate SaaS sources via APIs, use Databricks/PySpark/Delta Lake and AI tools to power lead generation, enrichment, deduplication, and entity resolution. Collaborate cross-functionally to translate business needs into automated, production-ready solutions.

Design, implement, and maintain dbt/Snowflake ELT transformations to convert raw clinical and multimodal health data into trusted, analysis-ready models; enforce data quality, lineage, testing, CI/CD, and documentation; collaborate with clinical and ML teams to harmonize ontologies and support de-identification and ETL across health systems.

Design, build, and maintain automation workflows and data pipelines across admissions, education, career services, and billing. Create BI dashboards, ensure data accuracy, manage lightweight databases (e.g., Supabase), and support cross-functional teams to improve operational efficiency and scalability.

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

The Product Analyst will design experiments, analyze user behavior, and communicate insights to drive engagement and retention decisions effectively.

The Manager of RevOps/Yield analyzes patterns and data to optimize ad operations, manage vendor relationships, and oversee a remote team for revenue generation.

The Senior Data Analyst will lead complex data analysis projects, mentor junior analysts, develop dashboards, and provide insights to drive strategic decisions. Responsibilities include troubleshooting data issues and communicating findings effectively.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.