Senior Analytics Engineer
Plaid is a financial data network and fintech infrastructure company that helps people securely connect financial accounts to digital financial services.
Funding history
Projects
About Plaid Inc.
Plaid provides developer infrastructure and financial tools for account connectivity, financial data access, bank payments, identity verification, AML monitoring, credit and underwriting, and fraud prevention. Its network supports thousands of fintech companies and more than 12,000 financial institutions across the United States, Canada, the United Kingdom, and Europe.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You'll be the technical owner of Plaid's Marketing data stack, building dbt models, predictive frameworks, and self-serve data products for marketing spend planning, performance measurement, and growth. You'll partner with PMM, Growth Marketing, and Marketing leadership on data models, LTV, lead scoring, experimentation, attribution, and AI-powered self-serve analytics.
Requirements
- Bachelor's degree in a quantitative field such as CS, Statistics, Economics, Engineering, or equivalent experience.
- 5+ 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 such as 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.
Responsibilities
- Own the dbt models and data marts that power Marketing analytics, activation, and reporting.
- Build, validate, and productionize predictive models such as lead scoring, LTV, channel attribution, and 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.
Benefits
- Medical, dental, and vision insurance
- 401(k)
- Equity