Top Senior Data & Analytics Jobs in Los Angeles, CA

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.

Design and deliver complex BI solutions and analytics to support Medicare risk adjustment programs. Build dashboards, reports, and data marts using SQL, Snowflake, and ETL (SSIS); apply advanced analytics and scripting (Python/Batch); gather requirements, perform testing and peer reviews, lead project/functional teams, and collaborate with DBAs, data modelers, and system admins to implement data strategy and solutions.

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

Lead product data analytics: analyze large datasets, build models and dashboards, design and evaluate A/B tests, advise product decisions, automate analytics with AI-enabled tools, and collaborate with global R&D and product teams to deliver actionable insights.

Lead analytics workstreams to extract insights from large datasets, build dashboards and predictive models, manage project planning and delivery, mentor teams, and collaborate with stakeholders to implement data-driven solutions and maintain data integrity.

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.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Lead and grow a data engineering team that builds and operates ingestion, transformation, orchestration, and serving layers and a self-serve analytics substrate. Own data quality, observability, cost efficiency, SLAs, and engineering practices while partnering with Data Science, BIE, Analytics, and Product to deliver reliable, reusable data products.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

Lead and grow a team of analytics engineers while remaining hands-on with SQL, dbt, and Snowflake. Own roadmap and semantic layer development, build trusted, governed data products, partner with cross-functional stakeholders, champion data quality and observability, and hire and coach engineering talent to deliver scalable analytics and AI-ready foundations.

Lead end-to-end COGS and gross margin analysis for hardware and software products, model cost-saving scenarios, answer ambiguous executive questions, partner with data engineering/finance/product, build reliable datasets and analytical frameworks, and present recommendations to C-suite stakeholders.