Senior Analytics Engineer

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.

The Analytics Engineering team owns the full-stack analytics foundation for Plaid's GTM, CGX, NEA and Marketing organizations. We build and maintain the core semantic layer data models (dbt on Databricks), activation layer, and BI surfaces that these teams rely on — and we partner with stakeholders to turn those models into decisions, forecasts, analytics and experiments.

As a Senior Analytics Engineer, you’ll also act as an applied data science partner. In addition to core analytics engineering, you’ll work on predictive modeling, experimentation, lifetime value (LTV), and attribution alongside the broader team.

As a Senior Analytics Engineer on the Marketing pod, you will be the technical owner of Plaid's Marketing data stack. You will build the dbt models, predictive frameworks, and self-serve data products that Marketing leadership uses to plan spend, measure performance, and drive growth.

You'll partner directly with PMM, Growth Marketing, and Marketing leadership to deliver core data models, frameworks, and tools — including LTV, lead scoring, and experimentation tooling — with a north star that aims for prescriptive and production-grade analytics. You'll also help build the AI-powered experiences that let Marketing partners self-serve from our metric layer

Responsibilities

• Own the dbt models and data marts that power Marketing analytics, activation, and reporting.

• Build, validate, and productionize predictive models (lead scoring, LTV, channel attribution, propensity) in partnership with Marketing and GTM stakeholders

• Partner with Marketing leadership on measurement frameworks, experiment design, and spend optimization — translating business questions into analytical answers

• Enable self-serve analytics through AI tools and well-documented semantic models

• Collaborate with ML, Data Engineering, and Ops teams to deliver best-in-class data infrastructure to Marketing

Qualifications

• Bachelor's degree in a quantitative field (CS, Statistics, Economics, Engineering, or equivalent experience)