Top Senior Analysis Reporting Jobs in Jacksonville, FL

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

As a Senior Forward Deployed Data Engineer, you will design and implement data pipelines for media agency clients, manage technical delivery, and ensure data security while mentoring junior engineers and communicating effectively with stakeholders.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

As a Director in the Data & Analytics team, you will lead client engagements, manage projects, develop strategies, create proposals, and mentor team members in data-driven initiatives.

As a Technical Solutions Manager, you'll onboard Enterprise customers onto Medallion's platform, focusing on data migration, integration, and automation for enhanced operational efficiency.

The Principal Data Platform Engineer leads the architecture and delivery of data platforms using Databricks, focusing on data pipeline design and client relationships.

Lead the technical roadmap and hands-on development of a scalable data platform, build analytics and data access solutions, ensure data quality/governance/observability, mentor and grow a team of data engineers, collaborate cross-functionally, and evaluate new big-data technologies.

The Senior Data Scientist will lead innovative data science efforts, leveraging advanced analytics, visualization, and insights from complex datasets to boost productivity.

As Measurement Lead, you will manage high-stakes customer discussions about campaign results, write SQL for data analysis, build dashboards, and collaborate with various teams to enhance Kard's measurement approach.

Design, optimize, and scale Snowflake-based data architecture and pipelines. Implement data observability, operationalize MLOps, build semantic enterprise models, and deploy AI-assisted agents and automation for production analytics and commercial data products. Share cross-functional ownership for pipeline resilience, monitoring, and model lifecycle management.