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

Lead encounter data operations: monitor submissions, correct rejected encounters per CMS/DHCS, write SQL queries and ad hoc scripts, generate reconciliation reports, analyze trends and root causes, validate EDI 837 resubmissions, manage relationships with health plans and clearinghouses, update documentation and training, and lead special projects to improve finance-related encounter workflows.

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.

Senior Data Analytics Consultant who turns observability data into business insights for enterprise clients. Manage 4–8 accounts, present performance strategies to technical and executive audiences, perform ad-hoc analysis, configure Dynatrace, build reports and dashboards, enable clients, influence product roadmap, and improve internal processes. Occasional client and annual team travel required.

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.

Design, implement, and validate data mapping, transformation, and integration solutions for payment integrity. Investigate and resolve complex data quality issues, create mapping specs and documentation, partner with stakeholders, and mentor analysts to improve data quality and automation.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

The Enterprise Data Architect will design and govern data architecture, create enterprise data models, and ensure data consistency across various systems.