Top Remote Data & Analytics Jobs in San Diego, CA

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

The Senior BI Analyst will gather requirements, develop scalable reporting solutions, and create dashboards using Power BI, ensuring data-driven decisions across the organization.

Assist in collecting, cleansing, and analyzing datasets to produce actionable insights, reports, and dashboards for informed business decisions.

Lead architecture, operation, and continuous improvement of cloud-native, enterprise-scale data platforms (Snowflake, cloud storage, streaming). Translate requirements into roadmaps, design and optimize data warehouses and lakes, automate operations with Python and IaC, mentor engineers, and deliver managed services across multiple client accounts while driving reliability, security, and performance.

The Data Scientist designs and builds data structures for AI applications, manages vector and feature stores, leads data discovery, and collaborates on responsible data use while ensuring high-quality, governed datasets for ML models and applications.

Design, build, and maintain scalable, reliable data systems and pipelines. Partner with Product, Sales Ops, and Finance to deliver analytics and self-service tooling. Maintain SLOs, improve pipeline reliability, implement testing and documentation, and contribute to data platform strategy and operations.

Customer-facing specialist guiding enterprise cloud data engineering and lakehouse transformations. Architect and optimize large-scale data pipelines, perform performance and load testing, support pre-sales POCs and workload sizing, lead adoption workshops, and mentor peers to drive Databricks platform adoption.

The Supply Analyst role involves analyzing marketplace data using SQL, making recommendations on product strategies, and leveraging AI tools to enhance processes and tracking.

Lead the architecture and implementation of enterprise-scale Azure data and AI platforms, including data lakes, lakehouses, warehouses, integrations, machine learning, and generative AI solutions. Establish governance, security, MLOps, and AI frameworks; guide cloud modernization, migration, performance, and cost optimization. Translate business needs into technical architectures, present recommendations to executives, lead architecture reviews, support client pursuits, and mentor engineering teams.

Lead process mapping, analysis, and redesign to improve efficiency and outcomes. Partner with stakeholders to gather requirements, analyze data (SQL, Excel, Power BI), define KPIs, apply Lean/Six Sigma methods, and support automation and digital transformation initiatives to drive growth, profitability, and loss reduction.