Top Analysis Reporting Jobs in Palm Bay, FL

The Senior Bioinformatics Data Engineer will build data pipelines, implement clinical data ingestion, and optimize platform infrastructure while ensuring data quality and compliance with legal standards.

Lead the design, development, and management of Workday integrations and Extend applications; oversee integration projects and ensure compliance with data security standards.

Lead design and delivery of a scalable, multi-product data platform. Architect and build robust batch and streaming pipelines, modernize legacy systems, improve observability, and partner with product, data science, and engineering to enable AI-driven solutions at scale.

Build and own the reporting and insight layer: write optimized Redshift queries, build dashboards, monitor model feedback signals, triage and prioritize investigations for data science, maintain ETL pipelines ingesting semi-structured data, and enable Customer Success with clear metrics and onboarding checks. Move into model validation and automation to scale analytic workflows.

Enter, verify, and maintain large volumes of data across databases, spreadsheets, CRM/ERP systems. Perform audits, resolve discrepancies, generate routine reports, organize digital files, and collaborate remotely while following data entry protocols and safeguarding confidential information.

Lead and build Newfire's clinical data engineering practice: define architectures, reusable patterns, and governance; own analytics and transactional pipelines; provide hands-on technical leadership with Databricks/Snowflake/dbt; mentor and grow the team; partner commercially to identify opportunities; and integrate practical AI capabilities for healthcare data use cases.

Lead design, build, and optimize scalable ETL/ELT data pipelines and analytics platforms using Databricks and Spark. Integrate and transform source data into analytics-ready layers, maintain data documentation and lineage, optimize SQL Server performance, collaborate with product and engineering teams, and apply AI-enabled tools responsibly to drive business insights.

Own support analytics for User Operations: build dashboards and recurring reports, track north-star metrics (cSAT, TTR, QA, escalations), instrument Zendesk, connect support to product/customer data, quantify operational costs of bugs, and measure AI-driven support performance.

Lead business intelligence efforts to analyze complex data, define KPIs, identify operational inefficiencies, and recommend process improvements. Collaborate with cross-functional teams and executives to implement data-driven strategies, manage improvement projects, monitor resource allocation, and ensure compliance with policies and regulations.

Design and maintain enterprise information architecture, including domain models, taxonomies, ontologies, and metadata to make data AI-ready. Operationalize models via enterprise data catalog (Alation), resolve semantic conflicts, define metadata standards, and partner with analytics and AI teams to enable governed, explainable AI and downstream BI/LLM use cases.