Kraken needs to build out cost attribution models and automation systems to power faster decision-making, tax readiness, and scalable financial intelligence. This role bridges the technical and the strategic — building the data foundations that enable Kraken to understand cost structures, improve ROI visibility, and launch products in new markets with lower risk and higher speed.
Requirements
- Proficiency in SQL and Python (pandas, numpy, matplotlib) for modeling, automation, and analysis.
- Experience building data models for cost allocation, ROI, or financial performance measurement.
- Familiarity with tax data structures, reporting processes, or regulatory data automation.
- Strong understanding of ETL workflows and orchestration tools like Airflow or dbt.
- Capable of creating intuitive dashboards and visualizations to communicate complex insights clearly
- Experience in accounting data systems, ERP integration, or tax technology solutions.
- Understanding of cost modeling for global or multi-entity organizations.
Responsibilities
- Build and own cost attribution datasets and models that quantify ROI across products, teams, and markets.
- Partner with finance, product, and data engineering teams to establish data pipelines for cost and revenue modeling.
- Develop scalable models that attribute people, infrastructure, and operational costs accurately across the organization.
- Lead automation of tax reporting data flows to ensure timely, compliant, and auditable processes.
- Create self-serve dashboards and data tools that empower finance and leadership with instant visibility into key metrics.
- Design robust data models that support financial planning, forecasting, and regulatory reporting.
- Collaborate cross-functionally to ensure data definitions, processes, and reporting are consistent across systems.
Other
- 3+ years of experience in data science, analytics, or financial data engineering roles.
- Ability to communicate financial and technical insights to stakeholders across all levels.
- Passion for enabling smarter financial operations through automation and data excellence.
- Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution.
- We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.
