Top Remote Senior Level Data Engineering Jobs in Los Angeles, CA

Lead client-facing data solutioning and pre-sales efforts for Consumer verticals, design executable data architectures, support pursuit to close, and provide ~50% hands-on delivery and architectural oversight while ensuring information security and data privacy compliance.

Serve as a clinical subject-matter expert to design, develop, and deploy production-grade data and analytic solutions detecting fraud, waste, and abuse. Bridge clinical, data, and technology teams; support data governance, interoperability, NLP/AI/ML, and build clinical business rules and analytics for enterprise use.

As a Data Architect, you'll design, implement, and maintain data infrastructure, and lead the delivery of new data solutions in a collaborative environment.

The Senior Data Analytics Engineer will enhance audit processes by integrating data sources, preparing datasets, mentoring teams, and managing the AWS platform.

The CJA Pod Lead/Technical Architect will lead the migration of Adobe Analytics to Customer Journey Analytics, ensuring accurate recreation of analytics logic and reporting structures. Responsibilities include guiding teams, overseeing data validation, and collaborating with stakeholders to optimize analytics environments.

The Lead Analytics Engineer will build a canonical data foundation for decision-making at Kit, enable self-service data exploration for stakeholders, and modernize the analytics process across departments.

Lead the Engagement Data Warehouse team, owning the vision for data products and managing a team of data engineers. Drive data roadmap, engineering excellence, and foster team culture within data-intensive systems.

As a Payment Analytics Engineer, you will ensure data consistency in payments, automate reporting, analyze interchange data, and collaborate with Engineering for data management.

Lead a team to integrate data and marketing solutions for nonprofit clients, ensuring data analytics accuracy and technical architecture alignment.

Design and maintain high-quality data pipelines, develop DBT models, collaborate with analytics and data science teams, and optimize data operations for scalability.