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 an 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 an 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)
- 4+ years of proven experience in analytics engineering, data science, or a closely adjacent function
- Advanced SQL and production-grade data modeling experience — dbt strongly preferred
- Python proficiency for modeling and analysis work
- Hands-on experience with a modern cloud warehouse (Databricks, Snowflake, BigQuery, or Redshift)
- Demonstrated experience shipping predictive models or applied ML in a business context
- Prior experience in Marketing Analytics, Growth, or GTM analytics at a SaaS or usage-based technology company
- Strong stakeholder communication and the ability to autonomously drive projects end-to-end
The target base salary for this position ranges from $141,600/year to $194,400/year [in Zone 1}. The target base salary will vary based on the job's location.
Our geographic zones are as follows:
Zone 1 - San Francisco / New York City / Seattle
The base salary range listed for this full-time position excludes commission (if applicable), equity and benefits. The pay range shown on each job posting is the minimum and maximum target for new-hire salaries. Actual pay may be higher or lower depending on factors like skills, experience, and relevant education or training.
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