Key Responsibilities

• Extract, clean, and analyze complex, medium-to-large scale datasets from diverse internal and external sources to uncover actionable trends and insights.
• Design, build, and maintain interactive dashboards, automated reports, and data visualizations that monitor key performance indicators (KPIs).
• Conduct in-depth exploration data analysis (EDA) and quantitative research to support strategic decision-making, product direction, and business initiatives.
• Translate complex statistical findings into clear, compelling narratives and recommendations for both technical and cross-functional stakeholders.

Required Technical Skills & QualificationsExperience:

• Proven professional experience as a Data Analyst, with a strong track record of independently researching and analyzing medium-to-large datasets.
• Data Querying: Advanced proficiency in SQL for complex querying, data aggregation, and transformation across relational databases.
• Programming for Analysis: Strong hands-on experience with Python (specifically libraries like Pandas, NumPy, Matplotlib/Seaborn) or R for statistical computing, data wrangling, and automating analytical workflows.
• Statistical Rigor: Solid foundation in applied statistics, hypothesis testing (e.g., A/B testing methodologies), and quantitative research techniques.

Bonus / Nice-to-HaveCloud & Modern Data Stack:

• Experience querying and working within cloud data warehouses (e.g., Snowflake, Google BigQuery, AWS Redshift) and utilizing data transformation tools like dbt.
• Big Data Ecosystem: Familiarity with distributed computing frameworks (e.g., Apache Spark) for handling exceptionally large datasets.
• Advanced Analytics: A foundational understanding of predictive modeling, forecasting, or applied machine learning concepts.
• Data Visualization: Deep expertise in building insightful and optimized dashboards using industry-standard BI tools & Spreadsheets.
• Tools: Modern development environment with AI-assisted tooling.