Senior BI Analyst

Role Overview The Senior BI Analyst plays a critical role in driving supply chain analytics by building key tools or applications and owning end-to-end reporting, data modeling, and data pipeline development that lead to insights and automations based on deep business domain knowledge that drive user efficiency. This role works closely with Business, Supply Chain, cross functional partners such as MP&A;, Finance, Merchandising and IT teams to translate business requirements into scalable data solutions and actionable insights.

The individual is expected to independently manage projects, deliver tools/applications, datasets, build robust reporting layers, and ensure data reliability across systems. In addition to executing analytics, the Sr. BI Analyst contributes to improving system and data architecture, standardizing KPIs, improving data quality and enabling self-service analytics.

This role acts as a “Builder”, strengthening the analytics foundation while mentoring junior analysts and improving business user effectiveness and productivity.

Core Responsibilities (Day-to-Day)

- Analytics & Reporting: Design, develop, and manage supply chain tools, optimizations, dashboards and reports using tools such as SQL, Snowflake, MicroStrategy, Python, and Sigma. Ensure accurate tracking of supply chain KPIs and provide actionable insights to stakeholders.
- Answer business problems using a mix of Explanatory and Exploratory Analysis and Storytelling using Visualization.
- Present findings to stakeholders in an easily consumable manner.
- Dashboard Maintenance & Monitoring: Design, initiate, maintain and enhance reporting assets based on converting data into insights. Proactively identify anomalies or data inconsistencies.
- Data Engineering & ETL: Develop and optimize SQL-based data pipelines in Snowflake or other tools. Structure datasets for scalability, performance, and reusability. Architect and implement scalable data pipeline solutions to optimize analytical frameworks and accelerate innovation and business insight
- Data Management & Validation: Collect, clean, and validate data from Enterprise Systems (e.g., SAP/Centric/Salesforce/Line Planning) and other sources.
- Domain Expertise: Work with business and product managers to influence tool and analytics roadmap to build capabilities ahead of business needs. Keep up to date understanding of business usage, the business processes it supports,



data meaning and future capabilities required by the business to drive competitive advantage
- Data Quality & Governance
- Participate in data governance activities, including dataset documentation, metadata maintenance, and data quality checks.
- Identify data issues and support root cause analysis efforts in partnership with senior team members.
- Help maintain curated datasets and support publishing content to the enterprise data catalog.
- Semantic Layer Development: Lead the creation of standardized data models and semantic layers to enable self-service analytics.
- Enterprise Integration Support: Integrate data from Enterprise Systems to ensure seamless data flow, improved visibility, and consistency across business functions. Work with IT organization to ensure data dictionary, data usage and corporate standards are met.
- Business Application Development: Leverage low-code tools such as Sigma, Salesforce, Power Automate and Python to build lightweight applications that improve internal workflows and reduce manual effort. Continue to refine existing processes and tool to reduce user friction, improve efficiency and performance
- Innovation: Leverages cutting edge technologies to creatively solve business challenges. Stays current with technology innovation.
- Cross-functional Enablement: Partner with business teams to drive data adoption and lead KPI definitions and usage.

Skills & Qualifications

- Education: Bachelor’s degree in Computer Science, Information Systems, Supply Chain, Analytics, or a related field.
- Experience: 5+ years of experience in Business Intelligence, Data Analysis, or Supply Chain Analytics.
- Technical Skills:
- Strong foundation in SQL (joins, aggregations, basic query optimization).
- Advanced Excel Skills.
- Experience in Data Warehousing and tools such as Snowflake (or similar platforms like Databricks/Hadoop).
- Proficiency in BI tools (MicroStrategy, Power BI, Tableau, Sigma)
- Proficient in Python (Pandas, basic scripting, Flask) or any programming language for data handling and scripting.




- Proficient with low-code/no-code tools like Sigma, Streamlit, or Power Apps.
- Strong expertise in data cleaning and validation techniques.
- Data Engineering: Experience in ETL pipelines and performance optimization
- Computer Science Fundamentals:
- Strong programming fundamentals, object oriented programming, and algorithms
- Strong experience with data modeling principles and relational database theory & normalization techniques
- Ability to apply data structures and appropriate usage (arrays, lists, dictionaries, trees)
- Analytical Skills:
- Ability to work with large datasets to identify trends, patterns, and anomalies.
- Strong attention to detail with a focus on data accuracy and quality.
- Advanced understanding of KPIs and metrics, preferably in a supply chain or operations context.
- Statistical knowledge that can be applied to metrics and machine learning algorithms.
- Business & Functional Understanding:
- Experience with supply chain, manufacturing, apparel or retail operations.
- Understanding of Enterprise systems such as SAP or similar platforms.
- Exposure to Product Development, Order Management, Sourcing/ Procurement, Inventory, Warehousing preferred.
- Soft Skills:
- Good problem-solving and logical thinking ability.
- Solid communication skills to collaborate with cross-functional teams.
- Ability to manage tasks independently and follow structured processes (SOP-driven environment)
- Willingness to learn and adapt in a fast-paced, data-driven environment.
- Prior experience mentoring junior analysts.
- Preferred: Agentic AI & Automation Skills
- Knowledge of Agentic AI concepts, including AI systems that can autonomously plan, execute multi‑step tasks, and interact with tools or data sources.
- Experience using AI copilots or agents (e.g., Microsoft Copilot, OpenAI-based agents, workspace automation tools) to streamline analysis, documentation, or workflow orchestration.
- Understanding of how AI agents integrate with data ecosystems, such as connecting to SQL warehouses, APIs, MCP, RAG or analytics tools to retrieve, transform, or explain data.
- Experience evaluating AI-generated insights, including validating outputs for accuracy, bias, completeness, and alignment with business logic.
- Interest in exploring emerging AI capabilities (e.g., autonomous reporting agents, automated data quality checks, or self-service analytics through conversational interfaces).

📌 Senior Business Intelligence Analyst (Bengaluru)
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