Analytics Engineer

QUICKEN INVESTMENT SERVICES, INC.•Menlo Park, CA

37d•$135,000 - $145,000

About The Position

Quicken is the leading solution for personal finance management software and is committed to helping people lead healthy financial lives. For over 40 years, more than 20 million customers have relied on Quicken to help them take control of their finances. We continue to focus on delighting customers and making Quicken products the most used, most loved, and most effective personal finance software. Our goal is to ensure that our customers can access and manage their financial lives, anywhere, anytime, and on any device. This is an opportunity to work at a customer-driven company with a collaborative team that values technical excellence, innovation, and creativity while working together and having fun! Come join the Quicken team! Job Summary We're hiring an Analytics Engineer, a data-powered problem solver who bridges the gap between business needs and analytical systems. You'll be the first line of insight generation: rapidly answering business questions, prototyping tools, and enabling stakeholders through AI-assisted self-service and scalable solutions. This role is ideal for someone who thrives on quick iterations, sharp analysis, and automating away repetitive work.

Requirements

2-5 years' experience in data roles (e.g. analyst, BI engineer, analytics engineer)
Fluency in SQL and experience with BI/reporting tools
Strong grasp of business fundamentals and metrics-driven thinking

Nice To Haves

Experience with AI tools, copilots, or scripting for automation is a strong plus
Prior exposure to dbt or light transformation logic preferred

Responsibilities

Partner directly with stakeholders across Product, Marketing, Finance, and Ops to answer pressing questions.
Use advanced SQL and analytical thinking to turn ambiguity into answers fast.
Develop quick prototypes (dashboards, reports, notebooks) that guide business decisions.
Build lightweight, scalable solutions like recurring dashboards, alerts, and data monitors.
Create and manage AI copilots or agents to enable business users to self-serve insights via natural language.
Curate datasets and build semantic layers that allow repeatable, self-serve exploration.
Maintain documentation and usage examples that teach others how to use available data effectively.
Work with data engineers to identify gaps in raw data or modeling.
Influence data architecture and metric definitions by bringing real user needs to the table.
Own and maintain existing business-critical reports across departments.
Ensure reports are accurate, trusted, and evolve with changing business logic.
Identify and retire unused or duplicative reports to streamline the analytics ecosystem.