Top Remote Analysis Reporting Jobs in Wilmington, NC

Serve as a strategic client-facing advisor for Zeta Data Cloud solutions: demo products, support audience curation, audit accounts, identify intelligence-driven opportunities, train client teams, contribute client feedback to the product roadmap, and support RFPs to drive adoption and growth for agency partners.

The Staff Data Architect will enhance Jellyfish's data platform through architectural design, automation, advanced observability, and efficient data governance, while collaborating across teams.

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

This role drives data-driven decision-making in product analytics, collaborating with cross-functional teams to generate actionable insights and evaluate new launches to enhance platform growth.

Design, build, and maintain end-to-end data models and pipelines to support CX operations, compliance reporting, and customer insights. Create dashboards and metrics, enforce data quality and governance, automate recurring workflows, and partner with stakeholders and broader data teams to deliver self-serve analytics solutions.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

As a Data Engineer II, you will build, scale, and optimize data platforms, focusing on ETL/ELT pipelines, and collaborate with data scientists and AI engineers to support data-driven applications and insights.

In this role, you'll build and scale high-throughput data pipelines, ensure data quality, and leverage AI for improved data operations. You'll also lead initiatives in a fast-paced environment focused on real estate data engineering.

Analyze auction data to identify performance drivers and risks, collaborate with commercial teams, and provide actionable insights to optimize DSP outcomes.

Build and maintain dbt models, write SQL to investigate and validate data, implement data quality checks, document lineage and metrics, resolve production issues (including on-call), and partner with analysts and business teams to deliver production-ready data products using modern warehouse tooling and AI-assisted frameworks.