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PlaidPlaidSan Francisco, CA

Analytics Engineer

Builds and owns dbt models, predictive frameworks (LTV, lead scoring, attribution), and self-serve analytics tools for Marketing at a fintech SaaS company. Partners with leadership on experiments, spend optimization, and AI-powered experiences. Requires 4+ years experience, advanced SQL/Python, cloud warehouses.

157k – 215k/yr
Hybrid4+ YOEData Engineering

About the role

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.

Skills

SQLdbtPythonDatabricksSnowflakeBigQueryRedshiftPredictive ModelingLead ScoringLtvChannel AttributionData Modeling

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