Product Data Analyst

Quick Facts

• Focus: product analytics—usage, feature adoption, and linking product data with costs, revenue, and customer retention

• Collaboration: Data & Analytics (Business Systems & Revenue Operations); close work with Product and Tech

• Output: KPIs, telemetry definitions, dashboards/models, and recommendations for product development and monetization

Description

Translate product questions into metrics, telemetry requirements, and data acceptance criteria, then analyze how usage and feature adoption relate to costs, revenue, churn, and retention. Build and operationalize key product metrics, explore relational data models, logs, events, and telemetry, and connect product data with pricing/billing and financial outcomes. Create semantic models and Power BI dashboards to deliver actionable insights for product growth and customer experience.

Responsibilities

• Develop and operationalize key product metrics

• Analyze feature adoption, usage patterns, AI Attach Rate, and interaction types (Human, AI Assist, Orchestrated AI)

• Explore relational data models, logs, events, and telemetry data

• Define telemetry, counting rules, and acceptance criteria for new and evolving product features

• Work with DWH requirements: data sources, data models, pipelines, and data-quality controls

• Build semantic models, product reports, and Power BI dashboards

• Join product usage data with pricing, billing, costs, revenue, churn, and retention

• Align product data definitions with finance and CRM data; define data requirements across PLM and SDLC with Product and Tech

• Convert analysis results into recommendations for product development, monetization, and customer experience

Requirements

• Bachelor’s degree in statistics, math, economics, IT, or a related field

• 2–5 years of experience as a Product Analyst, Data Analyst, BI Analyst, or similar

• Advanced SQL, Power BI, and Excel

• Experience integrating data from multiple sources (e.g., ODBC or APIs)

• Ability to define KPIs, product metrics, counting rules, and acceptance criteria

• Understanding of DWH architecture and collaboration with technical teams on data sources, models, pipelines, and quality

• Understanding of APIs, event-driven architecture, and ETL/ELT data pipelines

• Ability to analyze SaaS/AI operational and usage data (sales funnel, churn prediction, customer segmentation, usage patterns)

• Comfort collaborating with product, technical, and business teams

• Knowledge of Python for EDA

• English at least B2

Benefits

• Hybrid work model (3 days office, 2 remote) in Warsaw

• Flexible working hours and possibility to work remotely

• Funding for medical care and a sports card

• Stable collaboration with an established company with strong investor support

• Work on SaaS, telecom, data, and AI products and projects