Senior Fullstack Data

About the role

As a Senior Fullstack Data, you’ll bring seniority and end‑to‑end fullstack data expertise to a data team on a mission to turn data into impact.

Recent team achievements

• Built a fraud detection model that flags suspicious transactions in real time and reduces manual review.
• Developed a non‑profit segmentation model to help marketing personalize campaigns and improve conversion rates.
• Experimented with a recommendation system to optimize donation tip suggestions and boost nonprofit revenue.
• Improved data reliability and monitoring across our stack with automated anomaly detection and alerts.

Responsibilities

• Maintain and improve our ingestion pipeline.
• Discuss with business stakeholders to translate business needs into data requirements.
• Build models in dbt, build ML models and deploy them to production.
• Communicate your results and monitor the business impact in Metabase or Streamlit.
• Mentor the team to raise the bar for everyone at Zeffy.

Data team principles at Zeffy

• Impact first: We optimize for maximum business impact with minimal effort, what we call Zeffy perfectionism. We have no problem shifting priorities if we realize that the impact is higher or lower than expected. We are a small team of 2 and we need to be cautious about how we use our time to maximize our impact on the business.
• Trust and ownership: Every project has a clear owner with full autonomy to deliver results. Collaboration is encouraged, but accountability is clear. You are responsible for the project for the whole development cycle, from gathering the needs to designing the solution to implementing the solution to monitoring the impact.
• Decentralized organization: Everyone at Zeffy is autonomous at monitoring the performance of their team with data and answering their basic questions. The role of the data team is to build the foundations for the teams to increase this level of autonomy. It comes with a clear and documented data model and scoring and classification projects to bring interpretability in our data.

Our data stack

• Languages & Frameworks: Python, SQL, scikit‑learn, XGBoost, PyTorch / TensorFlow
• Data & MLOps: Snowflake, dbt, AWS, Metabase, Metaplane, Fivetran