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

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

The Log Specialist at Dynatrace leads log management initiatives, collaborates with sales, architects data ingestion solutions, and advocates for enhanced log consumption strategies.

The Sr. Analyst in Pricing Analytics drives data-driven strategies for GoodRx by analyzing pricing, collaborating with cross-functional teams, and developing dashboards to support business decisions.

The Senior Analyst in AML KYC will conduct detailed customer onboarding, monitor client relationships for compliance, and update KYC records while utilizing AI tools for efficiency.

The Senior Manager of Data Engineering will build a data platform, lead a team, and ensure data accessibility to support AI and business operations.

The Data Scientist in Finance will analyze financial data, create dashboards, collaborate on data engineering, and find improvements for the business.

The analyst will own planning for launches, build forecasting models, partner with stakeholders, and drive data-driven decisions using AI and analytics.

Build and lead the Compliance data function, owning data products for Customer Risk, Transaction Monitoring, AML, Sanctions. Design and implement data risk frameworks (quality, usage, lineage), enforce data contracts between producers and consumers, maintain end-to-end lineage with Engineering, and drive a risk-based data roadmap aligned with compliance requirements.

Lead development and continuous improvement of EHS safety management systems and DriveSafe program. Oversee driver scorecards, performance analytics, dashboards, and ad hoc reporting. Partner with EHS, operations, IT, and leadership to align systems, ensure data governance, drive projects, implement EHS technologies, train users, support compliance, and use data-driven insights to reduce incidents and improve safety outcomes.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.