Top Remote Analysis Reporting Jobs in San Luis Obispo, CA

Build, maintain, and monitor ETL/ELT pipelines in the Microsoft Fabric ecosystem. Implement medallion architecture, transform raw data into analytics-ready models, ensure pipeline reliability and data quality, troubleshoot failures, optimize performance, and support CI/CD and version control for data artifacts.

Builds and maintains analytical data foundations for lending, underwriting, pricing, originations, collections, and portfolio analytics. Develops reusable SQL assets, dimensional models, data marts, transformation workflows, reporting solutions, and business intelligence products using SQL, Python, dbt, Git, Snowflake, Power BI, and Sigma. Partners with technical teams, analysts, and business stakeholders to integrate data, standardize metrics, automate testing, and deliver reliable, documented, scalable analytical assets.

Designs, develops, and maintains GCP-based security data pipelines and data products. Builds data quality and auditing frameworks, integrates security tools through APIs, optimizes BigQuery schemas and costs, and deploys automated ETL/ELT workflows using Python, SQL, and GCP services. Partners with security teams to deliver reporting views and dashboards. Cribl Stream experience is preferred for processing, enriching, routing, and reducing security telemetry and logs.

Lead definition and evolution of cloud data architecture and analytics platform using a lakehouse approach. Design secure, scalable AWS data platforms, medallion data layers, data modeling standards, ETL/ELT and streaming pipelines, governance, observability, and AI/ML enablement. Drive tool selection, cross-team architecture reviews, mentor engineers, and enforce data security, compliance, and operational excellence across the organization.

Build scalable data pipelines, ELT processes, infrastructure, and dimensional data models supporting internal analytics and customer-facing data products. Implement data quality monitoring, auditing, alerting, warehouse optimization, and governed semantic layers for AI and LLM-enabled analytics. Partner with technical and business stakeholders, improve data processes using AI, and deliver reliable metrics from diverse data sources.

Lead a high-impact analytics function partnering with clinical and business leaders to apply advanced analytics, modeling, and BI to improve participant outcomes, operations, and financial performance. Manage and mentor analysts, design analyses and models, build stakeholder relationships, and communicate actionable insights to influence enterprise strategy.

Leads complex, cross-functional business intelligence initiatives from strategy and scoping through delivery. Partners with senior leaders across Sales, Marketing, Finance, and Operations to build scalable data products and actionable insights. Establishes standards for metrics, data modeling, governance, quality, and storytelling; designs data infrastructure using modern tools such as dbt. Drives data literacy, mentors analysts, contributes to analytics roadmaps, and ensures data assets are trusted, discoverable, secure, and fit for executive decision-making.

Designs, builds, documents, tests, and maintains large-scale data pipelines across platforms and sources. Performs ETL and ELT while ensuring data governance, security, quality, validation, anomaly detection, and compliance. Collaborates with architects, analysts, and stakeholders to translate business needs into technical solutions, develops data models, and mentors junior data engineers.

Designs and builds scalable, reliable, observable batch and streaming data pipelines and models. Partners with stakeholders on data integration, validates data quality, writes tests, participates in code reviews, documents solutions, troubleshoots production issues, and aligns technical work with business needs. The role requires strong programming, database, data modeling, governance, and Databricks knowledge, with preferred experience in Spark, AWS data services, CI/CD, testing, orchestration, and transformation frameworks.

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.