Top Remote Analysis Reporting Jobs in Washington, DC

Leads advanced data analysis to guide product and business decisions, develops metrics, dashboards, data products, and AI-enabled reporting systems, and translates findings into strategic recommendations. Partners with data engineers on dimensional data models, data governance, and quality controls. Mentors analysts, aligns roadmaps with business priorities, improves problem framing, and communicates insights effectively to technical and business stakeholders.

Own enterprise analytics audits, implementations, data quality, attribution, segmentation, SQL analysis, BI dashboards, experimentation measurement, and client reporting. Serve as the senior analytics partner for major accounts, present insights to executives, train client and internal teams, and help define analytics and experimentation services. The role requires hands-on Adobe expertise, autonomous prioritization, technical collaboration, and the ability to connect organic and AI search performance to pipeline and revenue.

Develops and maintains enterprise BI reports, dashboards, data models, and visualizations supporting Financial Planning & Analysis. Defines reporting requirements and data standards, analyzes and validates datasets, contributes to data governance, troubleshoots BI issues, and recommends reporting framework enhancements. Collaborates with IT data teams and stakeholders on data integration, documentation, training, and reporting performance improvements.

The Senior Clinical Trials IT Business Analyst bridges Clinical Development and IT, optimizing clinical trial technologies and managing system implementations, validations, and stakeholder engagement.

Conduct AI-powered web research, scraping, market analysis, and lead generation to identify clients, partners, sponsors, investors, and collaborators. Build and maintain intelligence databases, automate research workflows, verify and enrich data, analyze unstructured information, prioritize opportunities, and present actionable recommendations to leadership.

Clean, standardize, enrich, and import product data into the PIM while maintaining data integrity and governance standards. Oversee bulk ingestion processes, leverage AI and automation, support data model improvements, resolve product listing issues, and contribute to data hygiene and workflow modernization. The role also involves collaborating with client-facing teams, documenting processes, proofreading product data, communicating progress, and adapting to changing priorities.

Leads the architecture, design, and implementation of scalable GCP and Databricks data lakehouse platforms. Designs real-time and batch pipelines, data models, governance, cataloging, security, and performance optimization. Advises clients, translates business requirements into technical solutions, leads project teams, mentors engineers, reviews architectures, and balances strategic planning with hands-on proof-of-concept and troubleshooting work.

Owns enterprise-level analysis of client sentiment, health, retention, churn, and journey data. Develops measurement frameworks, health scores, executive dashboards, root-cause analyses, and leading indicators tied to revenue. Synthesizes qualitative and quantitative feedback, identifies systemic experience risks, supports lifecycle improvement initiatives, and presents data-backed recommendations to senior leaders and cross-functional teams.

Own paid social measurement and analytics across brand, consideration, and performance campaigns. Execute Conversion Lift and Brand Lift Studies, design geo-randomized experiments with data science, build KPI dashboards, evaluate creative and audience performance, and recommend budget and strategy changes. The role requires advanced SQL and Python or R, causal inference expertise, scalable reporting, and the ability to communicate insights to senior marketing leaders.

Build, operationalize, and design complex data solutions for multiple customer accounts. Responsibilities include data transformation, cleansing, governance, security, quality, source analysis, testing data movement and transformation code, troubleshooting defects and incidents, and educating end users. The role also requires guiding customers on technical decisions, collaborating across teams, and supporting data products and analytical environments.