Top Data & Analytics Jobs in Los Angeles, CA

Design and build scalable data infrastructure and pipelines using Azure Data Factory and Databricks. Implement data integration, transformation, modeling, and optimization. Validate data quality and security, collaborate with teams and clients, and contribute to client projects while developing technical skills and personal brand.

Lead design and build of data infrastructure, pipelines, integration, and transformation using Azure Data Factory and Databricks. Manage teams and client accounts, mentor staff, ensure project success, optimize databases, apply security best practices, and drive data architecture and innovation to deliver actionable insights.

Support Film & TV Studios analytics by partnering with stakeholders and engineering to analyze large datasets, build scalable dashboards and self-service BI, write advanced SQL, ensure data quality and pipeline readiness, and communicate insights. Drive adoption of enterprise analytics, contribute to forecasting, audience insights, experimentation, and ML-enabled use cases across marketing, distribution, finance, and business development.

The Staff Data Engineer will design and build data pipelines, reports, and tooling for the Concepts Lab, ensuring reliable AI systems and leading data modeling and analytics efforts. The role requires extensive experience in production data systems and collaboration across teams to transition concepts into production.

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

Lead pricing analytics for GoodRx Companion subscription: build forecasting and elasticity models, optimize pricing to drive signups and unit economics, create dashboards and reports, analyze fill patterns, and deliver actionable insights to cross-functional stakeholders to improve program performance.

Lead strategic insights and ad measurement projects, analyze complex datasets, craft narratives connecting brand lift to business outcomes, partner with clients and cross-functional teams, and drive process improvements and customer-facing recommendations.

The Senior Data Engineer will design and optimize large-scale batch and streaming data pipelines using Scala and Spark, oversee the data engineering lifecycle, and engage in technical discussions to improve system efficiency.

Partner with business teams to define analytics roadmaps, build dashboards and visualizations in Tableau, write complex SQL and ad hoc analyses, collaborate with Data Engineering to evolve the data warehouse, maintain a data catalog, set data quality and governance practices, define SLAs, improve analytics performance, and mentor junior analysts.

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.