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

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

The role involves leading Quote-to-Revenue processes, mentoring junior team members, and ensuring quality deliverables in finance systems and billing operations.

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.

Build and deliver end-to-end, production-grade data solutions: design and maintain scalable ETL pipelines, ingest from diverse sources, implement dbt transformations, ensure data quality and observability, optimize performance and cost, and partner with analytics, product, and engineering teams to drive business impact.

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

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

Lead design, build, automation, and maintenance of enterprise reports, dashboards, and Alteryx workflows. Analyze operational and transactional data, produce executive summaries, recommend metrics and process improvements, and own a report portfolio while partnering with stakeholders to drive data-informed decisions.

Lead Data Analyst responsible for advanced analytics, real-time dashboards, capacity modeling, and network performance insights. Collaborates with stakeholders to identify inefficiencies, run tests and pilots, and deliver actionable recommendations to improve service provider performance and network operations.

Lead design and implementation of an enterprise Databricks lakehouse, build scalable batch and streaming pipelines, enforce governance and CI/CD standards, optimize Spark workloads, operationalize ML with MLflow, manage cloud infrastructure and IaC, and mentor data engineering teams.