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Tier-1 brand, metro location, mid-level generalist data title, and broad technical requirements increase competition.
Core data and analytics tools are transferable across industries, though retail domain experience is advantageous.
Explicit 5–8 years plus mandatory BigQuery/SQL/BI/GenAI and pipeline skills create high filtering rigor.
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Translate complex business problems into analytical questions and develop AI-driven solutions to support forecasting, optimization, and automation.
Collaborate with business leaders across multiple domains to deliver actionable insights and recommendations using advanced analytics techniques (causal, predictive, prescriptive).
Manage large-scale data pipelines and maintain high data quality and accuracy using platforms like GCP BigQuery, Spark, SQL warehouses, and orchestration tools like Airflow.
5-8 years total work experience with 3-5 years in relevant data analytics roles.
Bachelor’s degree in B.Tech/B.E or Master’s in Statistics/Econometrics/Mathematics or equivalent quantitative degree (e.g., Math, Statistics, Data Sciences, Computer Science).
Proficiency in SQL, data warehousing, BI visualization tools (Power BI, Looker, Tableau), and programming languages such as Python, R, or Hive.
Experience working with large structured and unstructured datasets (Teradata, Oracle, Hive, HDFS) and knowledge of advanced analytics techniques (Regression, Time-series, Classification).
Experienced in applying Generative AI (GenAI) and Large Language Model (LLM)-based techniques for enhanced data analysis and automation.
Skilled in integrating AI-driven tools and workflows into analytical processes to scale and improve business decision-making.
Comfortable operating in a fast-paced, agile environment collaborating with cross-functional business stakeholders and translating technical insights for non-technical audiences.