Top Remote Senior Level Data & Analytics Jobs in Los Angeles, CA

Lead the data engineering team, develop and maintain data models and pipelines for risk domains, and manage data quality and governance.

As a Principal Product Analyst, you will lead product analytics for unsecured installment products, executing high-impact analyses, optimizing user journeys, and collaborating cross-functionally to drive data-informed product strategies.

This role drives data-driven decision-making in product analytics, collaborating with cross-functional teams to generate actionable insights and evaluate new launches to enhance platform growth.

The Lead Data Analyst will define KPIs, analyze data trends, build dashboards, and support new product launches, requiring strong SQL and Python skills.

Design and implement scalable go-to-market intelligence workflows and automation. Define business requirements for reports and dashboards, partner with Product and Analytics, improve Data Cloud tool adoption, document processes, support integrations, and deliver automation-aligned pre-sales and client-facing materials.

The Senior Data Engineer will build and maintain data pipelines, optimize data processing, and ensure data privacy and quality within the healthcare tech space.

As a Staff Product Analyst, you'll define analytics strategies, build measurement infrastructure, design experiments, and partner with product teams to boost self-serve conversion and product adoption.

The Senior Ad Ops Engineer develops and manages data products for marketing activation, focusing on data pipelines, quality validation, and integration with marketing platforms.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.

Lead CX analytics strategy and operations by standardizing metrics, enabling self-service analytics, and embedding AI in the analytics lifecycle to enhance decision-making processes.