As Qantev’s first Product Data Analyst, you will play a pivotal role in driving data-informed decisions across our product team.

Reporting to the Product Lead, you will be responsible for establishing a data-driven culture within the product team by implementing analytics tools, ensuring data accuracy, and providing actionable insights to guide strategic decisions.. 

Everything is yet to be built and established, offering an exciting opportunity to design data practices and processes from the ground up.

RESPONSIBILITIES 

• Define and monitor key product KPIs, ensuring alignment with OKRs.
• Implement and oversee web analytics tools, training PMs to use them autonomously.
• Model and monitor data accuracy within pipelines and databases.
• Size product opportunities and define ROIs to support PM decision-making.
• Provide strategic insights to guide roadmaps and OKRs.
• Build QA dataset seeds to improve product quality.

REQUIREMENTS

• Proven experience (+3 years) in data analysis and reporting, including implementing and monitoring web analytics tools such as Google Analytics, Mixpanel, or Amplitude.
• Proficiency in data manipulation and visualization tools, such as SQL, Python, Tableau, Looker, or Power BI.
• Experience in creating and maintaining data pipelines, ensuring data cleanliness, accuracy, and reliability, and setting up robust quality checks.
• Strong understanding of relational databases, data modeling, and statistical methods, including A/B testing and hypothesis validation.
• Excellent communication and collaboration skills, with the ability to train non-technical stakeholders and manage competing priorities effectively.
• Highly analytical and organized, with critical thinking skills to solve complex problems and derive actionable insights.
• Ability to design and implement scalable processes and frameworks to support a growing product team in becoming fully data-driven.
• Understanding of industrial or business operational KPIs, especially in the health sector, is a plus.

HIRING PROCESS

• Interview with the Talent Acquisition Leader
• Interview with Product Chief of Staff
• Case Study with a Data Scientist
• Interview with Product Lead