Data Analyst

Role overview

Key responsibilities

• Execute data analysis to support merchandising decisions using established frameworks and methodologies.
• Prepare data, run analyses, and summarize results for scenario and decision analysis.
• Work with senior analysts and stakeholders to understand business questions and analytical requirements.
• Develop and maintain reports, dashboards, and basic models to track performance and trends.
• Apply foundational predictive and diagnostic analytics techniques under guidance.
• Query and analyze large datasets using SQL and platforms such as GCP BigQuery or similar tools.
• Support data pipeline development and validation with data engineering teams.
• Ensure analytical outputs are accurate, consistent, and high quality.
• Communicate findings through AI summaries and basic data storytelling.
• Document analysis logic, definitions, and assumptions to support transparency and reuse.
• Follow data governance, privacy, and responsible AI guidelines.

Experience and qualifications

• Overall experience of 2–6 years, including 1–3 years of relevant experience.
• India track: B.Tech, B.E., or master's degree in Statistics, Econometrics, Mathematics, or an equivalent field.
• US track: bachelor's degree.

Required and preferred skills

• Hands-on SQL, including joins, volatile tables, and basic query tuning, with strong data warehousing and BI knowledge.
• Experience with at least one BI visualization tool such as Power BI, Looker, or Tableau.
• Working knowledge of structured and unstructured databases, including Oracle, Hive, and HDFS.
• Exposure to large-scale datasets, BigQuery, Spark, SQL warehouses, and Airflow or similar pipeline tools.
• Exposure to R, Python, Hive, or other open-source languages and databases.
• Understanding of regression, time-series models, classification techniques, and other analytical methods.
• Git experience and experience working in an agile environment.
• Problem-solving, communication, curiosity, teamwork, and self-motivation.
• Retail, merchandising, or marketing experience is a strong advantage.
• Basic understanding of generative AI, LLM applications, prompt engineering, RAG, and AI-assisted workflows.