Top Remote Analysis Reporting Jobs in San Luis Obispo, CA

Build and maintain dbt data models, semantic layers, quality checks, and metric definitions. Own QuickSight architecture and administration, including datasets, refreshes, access controls, performance, AI features, and the Mode migration. Develop payor outcomes reporting, ensure data accuracy for analytics agents, and partner with data science, clinical, and business stakeholders on AI-native BI infrastructure.

Design and implement data integration and data warehouse solutions using Big Data and open-source technologies. Build cost-efficient, performant cloud data pipelines, apply data modeling and ML concepts, and implement real-time streaming and aggregation patterns. Hands-on SQL/NoSQL and programming experience required.

Leads enterprise analytics engineering by establishing statistical standards, validation and QA practices, measurement frameworks, reporting standards, governance, reusable data pipelines, automation, and production-grade analytics solutions. Connects Commercial, Medical, Market Access, and other teams to enable scalable, trusted, compliant, and decision-focused analytics. Requires strong technical and cross-functional leadership, analytics infrastructure expertise, and the ability to build enterprise capabilities from ambiguity.

Design, build, and maintain scalable data lakehouse platforms and ELT pipelines. Improve engineering practices, develop automation/tooling, monitor performance, reduce costs, and support platform modernization across Azure and cloud data technologies.

Design, build, test, and maintain scalable cloud-based data platforms and ETL/ELT pipelines (batch and near-real-time). Implement data models, data marts, governance, metadata, CI/CD and IaC, optimize pipeline performance, support migrations/DR, produce documentation, and collaborate with architects, DBAs, and stakeholders to meet federal security and audit requirements.

Administer and optimize SQL Server databases while designing normalized schemas, mapping XML data, and developing SSIS-based ETL and migration pipelines. Responsibilities include database maintenance, backup and recovery, performance tuning, security controls, audit logging, testing, production cutovers, rollback planning, and ongoing migration support. The role also requires collaboration across development, testing, systems engineering, and business teams.

Lead design, implementation, and operation of a scalable, secure cloud data platform (data lake/warehouse/lakehouse). Build data pipelines, migrations, archival, IaC provisioning, CDC-enabled ingestion, governance, multi-tenant security, DR/HA, and performance optimization to support reporting, analytics, and AI/ML for a federal judiciary modernization program.

Lead the Sales Strategy & Analytics function to deliver reporting, KPI tracking, profitability and competitive analysis, and sales technology ownership. Partner cross-functionally to provide executive insights, optimize tools and processes, drive adoption, and develop a high-performing analytics team that improves sales productivity, profitability, and strategic decision-making.

Design and maintain company-wide dashboards, define KPI frameworks, implement tracking and data governance, write advanced SQL, partner with product and engineering, build predictive/AI-ready data layers, automate reporting, and present insights to leadership to drive operational and financial improvements.

Leads the analytics products team and oversees the development, integrity, maintenance, and delivery of dashboards, reports, models, extracts, and routine data deliverables. Delegates work while remaining hands-on enough to ensure product quality and technology alignment. Partners across revenue cycle business services and applies analytics, project management, change management, business transformation, facilitation, and process improvement practices.

Designs, implements, and maintains DataOps infrastructure and tools to ensure secure, resilient, and performant data pipelines and cloud storage. Automates workflows, monitoring, disaster recovery, and migrations while collaborating with product, DevOps, and infrastructure teams to meet business needs and compliance.

The Senior Data Analyst gathers business and analytics requirements, evaluates internal and external data sources, and ensures data quality, governance, lineage, and validation. The role maintains data dictionaries and master data, defines KPIs, reconciles dashboards, documents data transformations, identifies automation opportunities, and delivers actionable insights. It also supports privacy controls, disaster recovery requirements, platform cost optimization, stakeholder communications, and mentoring of less experienced team members.

Lead enterprise data analysis and governance: elicit requirements, define KPIs, maintain logical data dictionary and data lineage, validate and curate data, design ETL/data integration needs, produce dashboards and actionable insights, drive automation/AI-first improvements and mentor junior team members.

Analyze large healthcare datasets and develop complex SQL queries using Azure Synapse/Databricks. Build Power BI reports, produce monthly reconciliations and ad-hoc analyses in Excel/PowerPoint, support payments to value-based specialists, evaluate and expand value-based programs, automate processes with Power Automate/Power Apps, and communicate findings to senior leadership while spearheading new technologies.

Leads business intelligence, KPI reporting, and performance measurement for Part D risk adjustment programs. Defines business rules, metrics, reporting requirements, and analytic specifications; translates business needs into dashboards, recurring reports, and curated datasets. Partners with IT and enterprise data teams on scalable analytics, automation, governance, and controls. Validates BI outputs and communicates complex data insights to senior and executive leadership.

Designs, builds, maintains, and optimizes scalable data pipelines and ETL/ELT workflows using Azure Databricks, SAS, SSIS, SQL, Python, Spark, and Azure services. Integrates internal and external data sources, improves pipeline performance and reliability, troubleshoots processing issues, supports Big Data solutions, and partners with engineering, product management, SRE, analytics, and cross-functional teams to deliver enterprise data platforms.