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

Develop data models, analyses, dashboards, and automated reporting for a collectibles marketplace. Partner with engineering, product, and data platform teams to define business metrics, identify customer experience and growth opportunities, inform strategy, and lead initiatives that improve business performance.

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

Leads a client-facing healthcare analytics team that designs and maintains prior authorization decision automation programs. Consults PaaS clients, develops strategic roadmaps, translates requirements into technical blueprints, analyzes authorization and claims data, and builds KPI reports using visualization tools. Manages direct reports, improves analytics processes and AI tooling, partners with product and engineering teams, and ensures automation outcomes and client KPIs are achieved.

Designs and maintains scalable data infrastructure, ETL/ELT pipelines, canonical datasets, and AI context layers. The role improves data quality and alignment across sales and finance systems, investigates data inconsistencies, leads requirements through UAT and implementation, supports analytics self-service, and partners with cross-functional teams to deliver process improvements and reliable business data.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Staff Data Engineer responsible for setting technical direction, architecting scalable data platforms, leading cross-functional initiatives, improving reliability and performance, and enabling AI-assisted analytics and development. The role includes hands-on development with Python, SQL, cloud data warehouses, orchestration, infrastructure as code, Kubernetes, Kafka, and streaming pipelines, along with mentoring, technical advocacy, documentation, code reviews, and on-call troubleshooting.

Analyzes account portfolio whitespace, pipeline, deal performance, campaign attribution, rep productivity, budgets, forecasts, and expenses. Builds models and executive reporting to improve revenue planning, forecast accuracy, and account management decisions. Partners with Account Management, Finance, Marketing, Contract Administration, and regional leaders; validates Salesforce data and translates complex findings into actionable recommendations.

Leads Motorsports Data Infrastructure and Data Science teams while owning the strategy, architecture, and delivery of data platforms supporting telemetry, simulation, trackside, analytics, and AI/ML use cases. Responsibilities include managing managers, designing streaming and batch pipelines, productionizing ML models, setting engineering standards, managing Azure and Databricks capacity, protecting sensitive data, and partnering with racing and executive stakeholders. The role remains hands-on in architecture, coding, code review, incident support, and technical problem-solving.

Build and maintain dbt data models, SQL transformations, ingestion pipelines, orchestration, and Tableau dashboards in support of HR and enterprise analytics. Own data quality, metric standardization, privacy controls, Row-Level Security, documentation, CI/CD, and troubleshooting from source systems through reporting. Partner with stakeholders to define requirements and KPIs, support workforce forecasting, optimize BigQuery performance, and pilot AI-assisted analytics using Google Gemini.

Own marketing measurement from initial engagement through closed-won revenue. Manage cross-channel reporting tied to Salesforce pipeline, tracking across GA4, advertising platforms, SEO, and AI search. Validate multi-touch attribution, analyze marketing experiments, model budget impact, and recommend investment priorities. Present performance insights to marketing and sales leadership across global regions.