Lead Analyst, Marketing Analytics

Rivian•El Segundo, CA

2d•Remote

About The Position

Rivian is looking for a technically deep and business-savvy Lead Analyst to sit at the intersection of data engineering, marketing science, and strategic storytelling. Reporting to the Sr. Manager of Marketing Analytics, this role is the analytical engine behind Rivian's full-funnel marketing measurement—from brand awareness and purchase consideration through lead conversion and fulfillment. You'll work closely with fellow analysts, Data Engineering, Marketing Ops, and marketers across the organization to build the data foundations, causal models, and dashboards that drive how Rivian invests its marketing dollars.

Requirements

Bachelor's degree in Analytics, Statistics, Economics, Business, Engineering, or a related quantitative field; Master's degree preferred.
6+ years in Marketing Analytics, Marketing Science, or Growth Analytics, with a focus on high-consideration customer journeys, long-term LTV, and cross-channel measurement. Proven track record of leading collaborative projects with complex subject matter, handling user feedback loops, and building consensus among stakeholders with varying perspectives.
Expert-level SQL and Python for advanced statistical analysis including propensity modeling, survival analysis, and causal inference. Hands-on experience with Databricks, dbt, and AI coding tools such as Cursor AI and Claude Code.
Proficiency in Tableau and Hex to build reliable, executive-ready dashboards.
Deep functional knowledge of CDP platforms, CRM identity management, and behavioral event-stream tools; you understand how these systems come together to create a unified customer record and can diagnose gaps in data pipelines.
Experience establishing data validation processes, maintaining data dictionaries, and enforcing data integrity standards across complex, multi-source marketing datasets.
The ability to connect operational fulfillment data to top-of-funnel brand perception and future purchase intent—and communicate those connections clearly to both technical and non-technical audiences.
Comfortable operating in a fast-paced, high-ambiguity environment (e.g., new product launches, startup-stage operations) with strong attention to detail and a bias for action.

Responsibilities

Design the analytical framework that unifies brand awareness metrics, digital behavioral data, and lead-to-fulfillment conversion events. Define clear requirements with cross-functional stakeholders to determine the critical path for data-driven execution.
Lead the strategy for incrementality testing and attribution modeling to prove the true marginal impact of marketing spend, translating complex technical concepts into actionable insights for strategic initiatives.
Identify high-value user paths and friction points to inform automated engagement triggers and personalized customer lifecycles. Partner with marketers and channel owners to operationalize findings into campaign and lifecycle decisions.
Partner with Marketing Ops and Data Engineering to audit, optimize, and evaluate pipeline architecture for the MarTech stack (e.g., CDP, CRM, Ad-Platform APIs). Ensure seamless data flow into Databricks, establish control processes around ETL tasks, and maintain accurate documentation of the data dictionary.
Develop, manage, and maintain critical KPIs and executive dashboards, ensuring maximum reliability, data accuracy, and integrity of information across all marketing and fulfillment datasets.
Own data validation frameworks and data dictionary documentation; proactively identify and resolve data quality issues across marketing data pipelines to ensure analysts and stakeholders are working from a single source of truth.
Build consensus among stakeholders with varying perspectives—marketing, engineering, finance, and leadership—by leading user feedback loops, defining requirements collaboratively, and translating business needs into technical specs and vice versa.