Top Remote Analysis Reporting Jobs in Sacramento, CA

Own end-to-end data pipelines (ingest, dbt transformations, BigQuery warehousing) and consolidate the analytics stack into a single source of truth. Build and maintain dbt models with tests and documentation, establish data quality and observability, own product analytics and instrumentation, partner with GTM and marketing on reporting, and prepare for high-volume/streaming ingestion.

The Data Engineer will build and maintain scalable data pipelines, improve ETL processes, collaborate with AI/ML teams, and contribute to engineering culture.

The Data Engineer at Anteriad will design, build, and optimize data pipelines, manage data integration processes, support analytics efforts, and collaborate with stakeholders to deliver scalable data solutions.

Lead Tilt's Business Intelligence function end-to-end: manage a distributed BI team, own reporting data models and semantic layer, build and maintain a metric-and-metadata library with reference SQL, review dbt work and hold quality through code review, prioritize stakeholder requests, and grow team technical and business fluency.

Provide MEAL technical advice and capacity strengthening to country teams and partners across RAPID and EMPOWER projects. Support MEAL system design and implementation, digital data-collection tools (Kobo/CommCare), data management, analysis, visualization, feedback and complaints systems, proposal MEAL components, and documentation. Conduct trainings, capture lessons learned, and represent CRS with donors and partners to improve program quality and accountability.

Remote, part-time unpaid internship supporting the Data Department by manually clipping video and tagging image/frame locations to meet weekly quotas (~10-15 hours/week). Interns perform repetitive annotation tasks, contribute to research projects, and collaborate with baseball analytics and performance teams.

Senior individual-contributor analyst leading end-to-end, high-stakes analyses, designing dashboards and reporting, mentoring analysts, applying statistical methods and causal inference, partnering with analytics engineering to shape warehouse models, and translating findings for technical and non-technical stakeholders.

Analyze customer lifetime value, retention, and campaign performance; build input/output and financial models; create automated reports and visualizations; drive insights to optimize revenue and manage related projects and process improvements.

Lead and grow a data engineering team; define architecture and standards for ETL/ELT pipelines; hands-on design, code review, and development; ensure production reliability, monitoring, and incident response; partner with product and engineering on data roadmap.

Lead demand forecasting and performance analytics for eCommerce: build forecasting models, run scenario and promotional analyses, produce executive reporting, drive cross-functional predictive initiatives, and deliver market and competitive insights to inform planning and resource allocation.