Data Analyst

About the Role

Join the Data Analytics (DA) team at TII across domains such as Marketing and Digital, Merchandising, Supply Chain and Logistics, Store Operations, Finance and more . Here you'll collaborate with business leaders to turn data into insights that drive strategic decisions. You’ll be part of a fast-moving, high-impact environment focused on leveraging business intelligence and advanced analytics to solve real-world problems.

This role combines technical expertise with business understanding to uncover and communicate actionable insights using cutting-edge statistical and analytical techniques.

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