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Plaid is seeking a Senior Analytics Engineer to empower financial transformation through data analytics, predictive modeling, and collaboration with Marketing leadership.
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.
- 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.
- Bachelor's degree in a quantitative field (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 (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.
- Additional compensation in the form(s) of equity and/or commission are dependent on the position offered.
- Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k).
- Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location.