Top Remote Data & Analytics Jobs in Palm Bay, FL

Build and operate large-scale data pipelines, web-scraping systems, and data-collection tools for AI datasets. Maintain analytical databases, optimize queries, troubleshoot pipeline and data-quality issues, manage containerized workloads across Kubernetes and Linux infrastructure, and support CI/CD workflows. The role also involves documenting technical processes, researching improvements, and developing scalable APIs in a fully remote environment.

Leads the design and delivery of scalable data pipelines, dbt models, orchestration workflows, and analytical data models. Establishes data architecture, engineering standards, quality frameworks, governance, observability, and self-service analytics practices. Partners with stakeholders, leads technical reviews, mentors engineers, supports hiring, oversees analyses, and participates in incident response and continuous improvement.

Designs and maintains BI solutions, writes optimized SQL and stored procedures, and builds AI-assisted reports and dashboards using Power BI, Tableau, and Excel. Translates business questions into actionable insights for Sales and cross-functional teams, develops data models, supports reporting migrations, and ensures data quality, governance, documentation, and consistency across platforms.

Designs and maintains scalable cloud data architectures, pipelines, ETL processes, data warehouses, databases, and data models. Ensures data quality, accuracy, security, availability, and consistency across sources. Develops SQL queries for analytics and reporting, and documents pipelines, schemas, architectures, and models. The role requires expertise with Python, Java, Scala, R, SQL, relational and NoSQL databases, cloud platforms, and Snowflake.

Design and build scalable data engineering and AI agent governance capabilities across major cloud AI platforms. Develop ingestion, processing, ETL, observability, security, lifecycle management, and identity governance features. Evaluate emerging AI agent frameworks and interoperability standards, troubleshoot customer issues, document architecture and processes, and collaborate with engineering, security, compliance, product, and support teams. The role also involves improving distributed data systems, mentoring engineers, and leading technical initiatives.

Designs and maintains pipelines migrating financial data from legacy Oracle databases to Oracle ERP. Develops SQL and PL/SQL transformations, backend integrations, data mappings, reconciliation, testing, monitoring, troubleshooting, and documentation. Partners with accounting, business, and technical stakeholders to resolve issues and support large-scale financial transformation. The role also involves AI tool adoption, code reviews, solution design, deployment activities, governance, communication, mentoring, and project oversight.

Leads data strategy and analytics for the Risk/Credit team, translating complex datasets into insights for Credit, Operations, Marketing, Product, and Finance. Responsibilities include automating Python/R workflows, building Tableau dashboards, partnering with Data Engineering on Databricks workflows, conducting strategic analyses, monitoring data quality, presenting recommendations to leadership, and documenting analytical methodologies. The role requires strong SQL, cloud data warehouse, visualization, analytics programming, distributed computing, and version-control expertise.

Own end-to-end data platform architecture across ingestion, lakehouse, warehouse, transformation, modeling, governance, and analytics delivery. Build pipelines, Spark and SQL transformations, data models, and production code. Design Databricks or Microsoft Fabric lakehouses, implement Delta Lake and medallion architecture, establish access controls and governance, and integrate databases, SaaS applications, APIs, files, and legacy systems. Evaluate platform choices based on client workloads, licensing, skills, and cost.

Build and optimize end-to-end data pipelines, production-grade dbt models, BigQuery schemas, Fivetran integrations, and Looker semantic layers. Partner with clinical, operations, finance, and growth teams to translate business questions into analytics projects focused on acquisition, attribution, conversion, and funnel performance. Establish data quality, governance, documentation, testing, and version-control practices while mentoring analysts and supporting secure, compliant data use.

Lead the technical direction, architecture, and roadmap for the company’s data platform. Design reliable large-scale pipelines, establish standards for modeling, testing, observability, and orchestration, and mentor data engineers. Partner with engineering, product, analytics, and business stakeholders to align infrastructure investments. Lead incident root-cause analysis, evaluate data technologies, and support scalable analytics, machine learning, and real-time processing capabilities.