Data Analyst

Key Responsibilities:  Execute data analysis to support merchandising decisions using established frameworks and methodologies  Assist with scenario and decision analysis by preparing data, running analyses, and summarizing results  Work closely with senior analysts and stakeholders to understand business questions and analytical requirements  Develop and maintain reports, dashboards, and basic models to track business performance and trends  Apply foundational predictive and diagnostic analytics techniques under guidance  Query and analyze large datasets using SQL and data platforms such as GCP BigQuery or similar tools  Support data pipeline development and validation in partnership with data engineering teams  Ensure accuracy, consistency, and quality of analytical outputs  Clearly communicate findings through AI summaries and basic data storytelling  Document analysis logic, definitions, and assumptions to support transparency and reuse  Follow corporate data governance, privacy, and responsible AI guidelines Requirements / About You: Experience: Overall 2-6 years exp and relevant 1-3 years exp Qualification: TI : B.Tech / B.E. or Masters in Statistics /Econometrics/Mathematics equivalent US : Bachelors Skills required: 1. Hands on experience to Structured Query Language (SQL) syntax, including joins, volatile tables, and basic query tuning. And deep understanding of core DW/BI concepts. 2. Experience in at least 1 BI Visualization tool (i.e. PBI, Looker, Tableau) with ability to learn additional vendor and proprietary visualizations tools. 3. Working knowledge of structured (Oracle, Hive) and unstructured databases including Hadoop Distributed File System (HDFS) 4. Exposure to large-scale datasets using tools like GCP BigQuery, Spark, or SQL-based warehouses and data pipelines (using Airflow or similar tools) 5. Exposure to R, Python, Hive or other open-source languages/database 6. Understanding of analytical techniques (like Regression, Time-series models, Classification Techniques, etc.) to discover and measure key business drivers 7. Git source code management & experience working in an agile environment 8. Problem solving skills 9. Self-motivated and able to work in team settings in a fast-paced environment 10. Competent and curious to ask questions and learn to fill gaps 11. Good communication. 12. Experience with Retail, Merchandising, Marketing will be strong addons 13. Basic understanding of Generative AI (GenAI) and Large Language Model (LLM) based applications, including prompt engineering, RAG and AI-assisted workflows 14. Ability to leverage AI-powered tools to improve analytical productivity, automate Know More About Us here:

Behind one of the world’s best loved brands is a uniquely capable and brilliant team of data scientists, engineers and analysts. The Target Data & Analytics team creates the tools and data products to sustainably educate and enable our business partners to make great data-based decisions at Target. We help develop the technology that personalizes the guest experience, from product recommendations to relevant ad content. We’re also the source of the data and analytics behind Target’s Internet of Things (iOT) applications, fraud detection, Supply Chain optimization and demand forecasting. We play a key role in identifying the test-and-measure or A/B test opportunities that continuously help Target improve the guest experience, whether they love to shop in stores or at Target.com. Key Responsibilities:  Execute data analysis to support merchandising decisions using established frameworks and methodologies  Assist with scenario and decision analysis by preparing data, running analyses, and summarizing results  Work closely with senior analysts and stakeholders to understand business questions and analytical requirements  Develop and maintain reports, dashboards, and basic models to track business performance and trends  Apply foundational predictive and diagnostic analytics techniques under guidance  Query and analyze large datasets using SQL and data platforms such as GCP BigQuery or similar tools  Support data pipeline development and validation in partnership with data engineering teams  Ensure accuracy, consistency, and quality of analytical outputs  Clearly communicate findings through AI summaries and basic data storytelling  Document analysis logic, definitions, and assumptions to support transparency and reuse  Follow corporate data governance, privacy, and responsible AI guidelines Requirements / About You: Experience: Overall 2-6 years exp and relevant 1-3 years exp Qualification: TI : B.Tech / B.E. or Masters in Statistics /Econometrics/Mathematics equivalent US : Bachelors Skills required: 1. Hands on experience to Structured Query Language (SQL) syntax, including joins, volatile tables, and basic query tuning. And deep understanding of core DW/BI concepts. 2. Experience in at least 1 BI Visualization tool (i.e. PBI, Looker, Tableau) with ability to learn additional vendor and proprietary visualizations tools. 3. Working knowledge of structured (Oracle, Hive) and unstructured databases including Hadoop Distributed File System (HDFS) 4. Exposure to large-scale datasets using tools like GCP BigQuery, Spark, or SQL-based warehouses and data pipelines (using Airflow or similar tools) 5. Exposure to R, Python, Hive or other open-source languages/database 6. Understanding of analytical techniques (like Regression, Time-series models, Classification Techniques, etc.) to discover and measure key business drivers 7. Git source code management & experience working in an agile environment 8. Problem solving skills 9. Self-motivated and able to work in team settings in a fast-paced environment 10. Competent and curious to ask questions and learn to fill gaps 11. Good communication. 12. Experience with Retail, Merchandising, Marketing will be strong addons 13. Basic understanding of Generative AI (GenAI) and Large Language Model (LLM) based applications, including prompt engineering, RAG and AI-assisted workflows 14. Ability to leverage AI-powered tools to improve analytical productivity, automate