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.

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.

The Senior Data Analyst will lead metrics definition, reporting, statistical analysis, experimentation, dashboard management, and pricing analytics. The role involves building data pipelines, collaborating with data engineers on data assets and feature stores, investigating complex business questions, and translating analytical findings into strategic recommendations for product, pricing, and senior leadership. Strong SQL, statistics, econometrics, communication, and cross-functional collaboration skills are essential.

Lead enterprise data architecture across warehouses, data lakes, downstream applications, and AI workloads. Design data models, Medallion Architecture, ELT standards, semantic layers, APIs, access controls, and reusable datasets. Partner with business and BI teams to drive adoption, conduct architecture reviews, establish platform standards, and mentor data and analytics engineers. The role requires deep Snowflake, dbt, data modeling, pipeline, and semantic-layer expertise, with experience in AI data patterns and cloud infrastructure preferred.

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Own Toast’s Voice of the Customer program across customer segments, analyzing NPS, CSAT, CES, qualitative feedback, and business metrics. Identify root causes of customer pain, develop data-driven recommendations, improve CX measurement practices, and influence cross-functional teams to prioritize customer-focused solutions. The role also builds listening channels, promotes customer-centric decision-making, and aligns CX priorities with organizational goals.

Build Deepgram’s governed decision layer above warehouse gold tables. Responsibilities include modeling cross-system entities, owning semantic and metrics definitions, creating agent-facing data tools and MCP surfaces, implementing data quality assertions and reconciliation checks, evaluating AI agent accuracy, and answering business questions while leaving reusable models behind. The role requires production-grade SQL and Python, semantic-layer ownership, strong data modeling judgment, AI-native workflows, clear writing, and comfort building infrastructure from scratch.

Leads enterprise data and analytics strategy, architecture, modernization, and delivery across cloud and on-premises environments. Oversees data platforms, engineering, integration, governance, quality, lineage, security, and compliance. Partners with executives to define roadmaps and investments, manages cross-functional teams and vendors, enables AI/ML capabilities, and mentors data professionals while driving scalable, trusted data solutions and measurable business outcomes.