Senior Data Management Professional – Data Product Owner

We’ll Trust You To:
Own and evolve scalable frameworks and sophisticated strategies for instruction and evaluation task design, ensuring datasets remain fit-for-purpose for complex Generative AI behaviors.
Align data frameworks and evaluation strategies with overarching product objectives to guarantee trustworthy, consumable intelligence that supports actionable user decisions.
Act as the primary multi-functional liaison, driving alignment between Product, Engineering, and Data teams to translate technical complexities into actionable insights.
Partner with multi-functional teams to define product-aligned requirements and reusable evaluation rubrics, ensuring outcomes meet rigorous Data Quality standards.
Drive the strategic evolution of our evaluation infrastructure by pioneering reusable, automated frameworks that consistently accelerate multi-functional product delivery.
You’ll Need to Have:
Bachelor’s degree or equivalent experience in Finance, Business, Economics, Accounting, STEM or degree-equivalent qualifications
A minimum of four years of demonstrated experience in data management concepts, including data quality, modeling, and random sampling
Extensive experience using data visualization tools such as Tableau or Qlik Sense to communicate sophisticated results to partners in a clear, concise manner
Demonstrable experience in Data Profiling/Analysis using tools such as Python, R, or SQL
Past project/experience analyzing financial datasets or demonstrable experience working on financial market concepts
A logical approach to problem-solving with the ability to resolve complex annotation and data-architectural challenges
Keen interest in and familiarity with generative AI frameworks and the requirements of Agentic AI
Excellent stakeholder management and project leadership skills, with a demonstrable ability to evaluate design trade-offs and seamlessly translate technical complexities between Engineering, Product, and Data teams.
Experience in data management concepts such as data quality, data modeling, and data engineering