Marketing Analytics Engineer — Predictive Modeling Lead

Plaid; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Plaid is seeking a Senior Analytics Engineer to own the Marketing data stack, build dbt models, and deliver predictive frameworks that guide spend and growth. You will partner with PMM and Growth Marketing to create prescriptive analytics and self-serve data products.

You will work with ML, Data Engineering, and Ops teams to ensure production-grade analytics and scalable data infrastructure across GTM, CGX, NEA, and Marketing initiatives.

Qualifications

• 5+ years in analytics engineering, data science, or similar
• Advanced SQL and production-grade data modeling (dbt preferred)
• Python proficiency for modeling and analysis
• Experience with modern cloud warehouses (Databricks, Snowflake, BigQuery, Redshift)
• Demonstrated delivery of predictive models in a business context
• Experience in Marketing Analytics, Growth, or GTM analytics at SaaS
• Strong ability to communicate with stakeholders and drive projects end-to-end

Responsibilities

• Own the dbt models and data marts powering Marketing analytics, activation, and reporting
• Build, validate, and productionize predictive models (lead scoring, LTV, channel attribution, propensity)
• Partner with Marketing leadership on measurement frameworks and spend optimization
• Enable self-serve analytics through AI tools and semantic models
• Collaborate with ML, Data Engineering, and Ops teams to deliver robust data infrastructure

Skills

SQL

Python

Stakeholder communication

End-to-end project delivery

Analytical thinking

Education

Bachelor's degree in quantitative field

Tools

dbt

Databricks

Snowflake

BigQuery

Redshift

Job description

Plaid is seeking a Senior Analytics Engineer to own the Marketing data stack, build dbt models, and deliver predictive frameworks that guide spend and growth. You will partner with PMM and Growth Marketing to create prescriptive analytics and self-serve data products.

You will work with ML, Data Engineering, and Ops teams to ensure production-grade analytics and scalable data infrastructure across GTM, CGX, NEA, and Marketing initiatives.