Top Remote Data & Analytics Jobs in San Luis Obispo, CA

Conduct quantitative research for traders by testing market hypotheses, building and maintaining documented signals, performing event studies and backtests, and validating results against leakage, survivorship bias, transaction costs, and regime dependence. Organize reproducible point-in-time datasets, communicate findings clearly, track signal performance, and use AI tools to accelerate research and coding while verifying outputs.

Lead cross-functional analytics initiatives across product, marketing, audio development, and business operations. Build data models, dashboards, self-serve reporting tools, and actionable company-wide metrics. Analyze activation funnels, A/B tests, growth KPIs, and product health while optimizing the analytics infrastructure. Partner with executives, engineering, product, marketing, and data engineering teams to improve governance, data adoption, and decision-making.

Develop enterprise Power BI solutions from discovery through adoption. Responsibilities include semantic modeling, advanced DAX and Power Query development, dashboard UX/UI design, governance, performance optimization, stakeholder workshops, client presentations, usability testing, adoption tracking, proposal support, and mentoring junior team members.

Lead strategic insights and market research projects for B2B clients, translating quantitative survey and statistical analysis into actionable recommendations. Manage client engagements, design surveys using methodologies such as conjoint and MaxDiff, present findings to nontechnical stakeholders, develop commercial offers, and coach Associates. The role supports marketing, product development, and strategic planning while helping build NewtonX’s professional services capabilities.

Lead the architecture and implementation of complex cloud-based data solutions for customers. Define strategic data roadmaps, support pre-sales engagements as a data engineering subject matter expert, interview and mentor architects and engineers, and oversee data standards and operating procedures. The role requires extensive AWS data architecture experience across traditional and modern data platforms, including big data, machine learning, generative AI, DataOps, infrastructure as code, CI/CD, and Python automation.

The Analytics Architect will design and implement cloud-based analytics solutions, maintain data pipelines, ensure data quality, and collaborate with teams to enhance operational efficiency.

Leads Magellan Health’s enterprise data and analytics strategy, modernization, engineering, governance, business intelligence, and AI/ML enablement. Oversees data platforms, pipelines, quality, lineage, compliance, and reporting while partnering with clinical, operational, and financial stakeholders. Builds and develops a 30+ person organization, manages a $3M–$5M budget, oversees vendors, and presents investment cases to executive leadership. Drives transformation from a low-maturity environment to a governed, insight-driven enterprise data platform.

Manage Advancement data systems, donor and prospect information, fundraising portfolios, reporting, analytics, gift processing, and reconciliation. Maintain donor database integrity, security, and adoption; conduct prospect research and wealth screening; produce dashboards and KPI reports; train staff; and collaborate with Finance and fundraisers to optimize donor engagement and fundraising operations.

Manage secure, compliant data environments, including governance policies, data architecture, integration, quality, security controls, audits, and performance optimization. Lead data analysis, reporting, documentation, and cross-functional collaboration while mentoring junior staff. The role requires protecting sensitive or classified data, maintaining access controls and encryption, and ensuring compliance with government and organizational standards.

Design, build, and optimize enterprise ETL/ELT pipelines, cloud data lakes, lakehouse solutions, data models, and semantic layers across AWS and Azure. Ensure data quality, security, performance, reliability, and governance while supporting analytics, reporting, and AI initiatives. Collaborate with product, engineering, IT, and business stakeholders to translate requirements into scalable data solutions, monitor pipeline health, troubleshoot issues, and improve data architecture and practices.