Top Remote Analysis Reporting Jobs in Baltimore, MD

Manage daily Data team operations, research and analyze datasets, build internal and external reports (including an annual Startup Ecosystem Ranking), and maintain/improve Python scripts and data infrastructure to deliver actionable insights.

Design and build Snowflake transformation layer and migrate legacy SQL to versioned dbt models. Define conformed dimensions/facts using Kimball principles, set modeling and CI/CD standards, implement data quality/lineage, support BI migration and AI/ML readiness, and mentor future hires. Hands-on work in dbt, SQL, and Python with stakeholder collaboration and governance for financial and operational data.

Build and maintain CS reporting, dashboards, and health score models to identify renewal risk and inform strategy. Deliver executive reporting, collaborate cross-functionally to improve data quality, and develop predictive analytics and longitudinal tracking to reduce churn.

Manage and mentor a data engineering team while leading Snowflake-based platform architecture, enterprise data integrations, governance, observability, and pipeline reliability. Partner with analysts, business leaders, product management, and engineering teams to improve trusted self-service data access. Establish engineering standards, modernize the data stack, optimize costs, and deliver an AI-ready data foundation supporting analytics, reporting, and future AI initiatives.

The Data Analyst will support data manipulation, ingest large volumes of data, create visualizations, and conduct analyses for U.S. Customs and Border Protection.

Lead design and implementation of the commercial data and systems foundation for launch and growth. Own Salesforce-led commercial systems, integrations, data warehouse, MDM, analytics enablement, governance, and scalable delivery to support commercial operations, reporting, and compliance.

The Analytics Engineer will design and implement analytics solutions, maintain reporting dashboards, partner with Product on KPIs, and support marketing with analytics.

Lead market and client-level fundraising data analysis and publish original research and benchmarks. Partner with Sales, Customer Success, and Marketing to produce ROI cases, client insights, and thought leadership. Present findings publicly at conferences, build repeatable insight frameworks and tools, and translate data into persuasive narratives for advancement leaders.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

The Databricks Architect will design and implement large-scale data solutions, optimizing performance and reliability, while leading initiatives in data engineering.