Data Analyst
Summary
Data analyst working with product, growth, finance, and operations leadership to diagnose churn/growth drivers and surface opportunities. Builds version-controlled SQL models (dbt-style), designs dashboards (Looker/Tableau/Power BI etc.) on cloud data warehouses, and sets up automated, AI-assisted pipelines and data governance. Remote from Warsaw.
Key Responsibilities
Insight Generation & Business Strategy: Partner with cross-functional leadership (Product, Growth, Finance, Operations) to identify trends, diagnose churn/growth drivers, and discover high-value business opportunities.
Analytics Engineering & Data Modeling: Write clean, modular, and version-controlled SQL models (using tools like dbt) to transform raw data into well-structured, production-ready tables.
Modern Visualization & Storytelling: Design intuitive, dynamic dashboards and narrative reports (using Lightdash, Looker, Metabase, Tableau, or Power BI) that drive decision-making rather than just display metrics.
Data Quality & Governance: Establish data definitions, document metrics in a central data catalog, and ensure high standards for data accuracy, testing, and reliability.
AI & Workflow Automation: Leverage AI-assisted analytics workflows and automated pipelines to speed up data exploration and deliver real-time operational alerts.
Requirements & Qualifications
Experience: 2–5 years of experience in a data analytics, product analytics, or business intelligence role.
Advanced SQL & Modeling: Strong mastery of SQL (window functions, CTEs, performance optimization) and familiarity with modern data transformation practices.
Modern Data Stack: Hands-on experience with cloud data warehouses (e.g., Snowflake, BigQuery, Databricks) and BI/semantic layer tools.
Business Acumen & Storytelling: Ability to translate complex quantitative findings into clear, actionable recommendations for non-technical stakeholders.
Analytical & Statistical Mindset: Solid understanding of basic statistical concepts (hypothesis testing, confidence intervals, regression analysis).
Version Control & Collaboration: Basic knowledge of Git/GitHub workflow for managing analytics code.
Nice-to-Haves
Experience using dbt (data build tool) for analytics engineering.
Proficiency in Python or R for exploratory data analysis, automation, or statistical modeling.
Experience with product analytics platforms (e.g., Mixpanel, Amplitude, PostHog).
Background in agile/scrum environments.