About the role

Join AlphaSense as a Product Analyst in Financial Data, supporting product management decisions. Collaborate with engineering teams for data-driven solutions in financial markets.

Responsibilities

Support development and execution of the FinData product roadmap.
Translate product goals into clear, actionable requirements for engineering teams.
Develop mockups, wireframes, and schematics to communicate concepts and requirements.
Define business logic and relationships to maintain accurate and predictable system behaviors.
Query internal PostgreSQL/Snowflake/Athena databases to identify key data-related insights and/or high impact data gaps.
Conduct detailed screening and data validation to support robust quality-assurance practices.
Propose design concepts and enhancements to address technical challenges and optimize system performance.
Develop and maintain product documentation to ensure alignment between all stakeholders.
Coordinate project prioritization, dependencies, and sequencing across teams and initiatives.
Support project planning activities, including timeline estimation, scheduling, and tracking.
Contribute to product and feature release documentation and communicate new functionality to internal stakeholders.
Work collaboratively with engineers, analysts and product managers.

Bachelor’s degree in a highly analytical field such as Engineering, Computer Science, Business, or a related discipline.
1–3 years of relevant experience, including internships, or academic project work.
Interest in building a career in product management and technology development.
Intellectual curiosity about financial data and capital markets intelligence.
Proven analytical mindset with the ability to translate complex problems into structured, data-driven solutions.
Excellent organizational skills and attention to detail.
Ability to meet deadlines and to work independently.
Foundational proficiency in SQL to navigate and query financial datasets is required; experience with advanced SQL techniques and Python for data analysis or automation is considered a strong asset.