Top Analysis Reporting Jobs in Sacramento, CA

Support randomized control trials and A/B tests for Party at the Polls: maintain experimental data tables, prepare samples, analyze results (pre/post-election), prioritize sites, and produce reports and timely program performance analysis.

Owner of cross-functional analytics projects: build and maintain dbt models, write advanced BigQuery SQL, create BI dashboards (Omni/Power BI), monitor data quality, define metrics, enable stakeholder decisions, and use AI tools to accelerate analysis and QA.

Lead and conduct qualitative and quantitative research and program evaluation for legal education initiatives; manage data collection, cleaning, and analysis; produce reports, tables, and visualizations; advise colleagues and stakeholders on research design and interpretation; support LexScholars program administration and maintain research repositories; present findings and assist with research tool development.

The Senior Data Engineer will design and build data pipelines and infrastructure to support data science and business intelligence solutions, utilizing AWS services and streaming technologies.

Lead the technical development and automation of enrollment forecasting models, collaborating with various departments to enhance decision-making and forecast accuracy.

The SAP IBP Analyst will optimize supply chain planning processes in e-commerce, focusing on SAP IBP modules, system integrations, and stakeholder management.

The Forward Deployed Marketing Data Scientist will work with AI customers and engineering teams to optimize AI-driven marketing campaigns through data analysis, diagnostics, and communication of insights to stakeholders.

Lead development and scaling of financial analytics: build data models, dashboards, and reporting; translate financial data into executive insights; embed predictive analytics; partner with Finance, IT, and business units to improve data quality, governance, forecasting, and automation.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Design and implement enterprise data architectures and lakehouse/warehouse solutions using Microsoft Fabric, Databricks or similar platforms. Lead data modeling, ETL/ELT pipelines, cloud integration, CI/CD, and stakeholder-facing consulting engagements to deliver scalable, secure analytics systems.