Product Analyst

Toast

9d•Remote

About The Position

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. As a Product Analyst for Toast you’ll partner closely with product teams to understand customer behavior and product performance. You will support data-informed decision-making by building analyses, dashboards, measurement frameworks and using AI tools to help teams evaluate product changes and identify opportunities for improvement. We operate like a small startup with high ambitions and a focus on experimentation, testing, and iteration - getting the data right is a key to our success. Your work will contribute to the success of our businesses. You love solving complex problems and getting to the ground truth for what’s happening in our product and with our customers. You enjoy digging into data and tying different sources together to answer business questions and uncover new insights. You’ll operate within our analytics team while supporting a specific line of business and partner directly product managers, engineers, designers, and data engineers. Most importantly, you’ll deliver scalable insights that the team can use to delight our customers. The Product Analytics team sits inside of R&D Operations at Toast. The R&D Operations team is on a mission to empower our teams to build great products efficiently, effectively, and at scale. We remove obstacles, foster alignment, and help teams focus on what matters most—solving real problems for our customers. Operating at the intersection of Product, UX, Engineering, Marketing, and Customer Success, we ensure that teams are equipped with the right processes, strategies, and insights to drive meaningful impact.

Requirements

Bachelor's degree in Business, Computer Science, Information Systems, or a related field with a minimum of 5 years of experience in Product Analytics, Data Analysis, Data Science or related field; or a Master’s degree with 3 years of related experience; or equivalent experience.
Strong SQL & statistical data modeling skills (e.g. SQL, Python, R) and experience working with large data sets ensuring accuracy and reliability.
Strong experience with analytics and visualization tools (e.g. Amplitude, Heap, Tableau, Looker, Hex, or similar.
Understanding and application of product metrics, funnels, behavioral analytics concepts, and experimentation.
Strong communication and storytelling skills, with the ability to distill complex data into clear recommendations for technical and non-technical audiences.
Experience translating stakeholder needs and requirements into clear analytical approaches, data architecture, dashboards and insights.
A self-starter mindset - comfortable building new analytics foundations and iterating on them in a fast-moving environment.

Nice To Haves

Experience working in a large-scale data environment, high-growth tech or SaaS company.
Experience with data modeling, ETL processes, and analytics engineering practices.
Experience with Hex, Looker, Snowflake, Sigma, Airflow, GitHub, AWS, dbt, python and/or equivalent toolsets plus AI tools.
Familiarity with Financial and Accounting concepts, data and reporting.

Responsibilities

Partner cross-functionally to support analytics needs for a specific product area or initiative.
Write SQL queries and use analytics tools to create datasets and analyses that measure product usage, performance, and customer outcomes.
Build and maintain dashboards and recurring reports that help teams monitor key metrics and trends.
Support experimentation efforts by helping define success metrics and analyzing results of A/B tests and product experiments.
Develop a strong understanding of product and operational data sources to ensure accurate and consistent reporting.
Enable self-service capabilities and build on trustworthy datasets.
Perform data quality checks and proactively identify anomalies or inconsistencies and improve data processes.
Translate analytical findings into clear insights and recommendations for product teams.
Contribute to shared analytics documentation, metric definitions, and best practices.
Proactively identify areas outside the regular stream of business requests where analytics can add value.