Lead BI Analyst

Job Title: Lead BI Analyst
Role Overview The Lead BI Analyst is responsible for driving the overall analytics strategy for Supply Chain Operations. This role owns the design and development of scalable tools, data models, governance frameworks, and reporting ecosystems while ensuring alignment with business goals. This role works closely with Business, Supply Chain, cross functional partners such as MP&A, Finance, Merchandising and IT teams. The individual acts as a bridge between business and technology, leading cross-functional initiatives, standardizing data management, and enabling data-driven decision-making across production, sourcing, and planning functions. This individual leverages cutting edge and proven technology to envision and develop tools and analytics that drive quantitative and qualitative business impact leveraging their deep business knowledge and technology experience. This role acts as an “Owner & Enabler”, setting direction, ensuring quality, and building long-term analytics capabilities as well as hands on development. Core Responsibilities (Day-to-Day)

• Data Modeling & Semantic Layer Development:

o Architect, oversee and build scalable data pipelines and data models in Snowflake.
Ensure best practices in performance, modularity, and maintainability. o Design, build, and deploy data cubes, semantic layers, and project tables within the Supply Chain MicroStrategy and other environments to support analytics, reporting, and advanced modeling use cases. o Design and maintain conceptual, logical, and physical data models based on business reporting and analytical requirements. o Partner with business stakeholders and analytics teams to understand end-use requirements and translate them into scalable data structures.

• Analytics Enablement & Business Partnership

o Collaborate with operational and business analytics teams to align on standard metrics,
KPIs, and data definitions, ensuring consistency across the enterprise. o Serve as the domain data expert for Supply Chain initiatives, providing guidance on data design, sourcing, and interpretation for projects impacting the flow of goods. o Act as a trusted advisor to analytics and modeling teams, ensuring data assets are fit for purpose and optimized for performance. o Customizes visualizations and user interface that drive insights from data and increase user adoption, speed to value and efficiency

• Supply Chain Digital Twin & Data Platform Ownership

o Drive continuous improvement of the Supply Chain Digital Twin, in partnership with IT
and Enterprise Data Teams. o Ensure data assets are performant, scalable, and aligned with evolving business needs. o Evaluate and recommend new data technologies, tools, and architectural patterns to enhance data platform capabilities.

• Data Quality & Governance

o Champion the data governance program for the Supply Chain domain, including:

• Vetting and defining curated datasets
• Promoting trusted content within the enterprise data catalog
• Enforcing data standards and best practices

o Proactively identify, troubleshoot, and resolve data quality issues through root cause
analysis and corrective action plans. o Establish monitoring frameworks, and governance processes to ensure systems are healthy and analytics are up-to-date.

• Domain Expertise: Identifies ways for technology, working with the business and product

managers, to drive competitive advantage within the domains they support. Understands
business strategy and works with product managers to co-develop technology roadmaps. Stays abreast of industry changes.

• Analytics & Reporting: Own the design, delivery and governance of executive ready

dashboards and reporting frameworks that translate findings into actional insights. Ensure
consistency, scalability, and alignment of KPIs across business functions.

• Business Application Development:

o Leverage low-code tools such as Sigma, Salesforce, Power Automate and Python to
build lightweight applications that improve internal workflows and reduce manual effort. o Develop supply chain models such as forecasts, linear programming optimization or machine learning models o Lead cross-team engineering initiatives, proactively identifying architectural risks, system bottlenecks, and scalability constraints. o Ability to analyze bottlenecks and drive workflow automation and process optimization using data-driven insights.

• Innovation: Experiments with new cutting-edge technologies to understand application to

business challenges. Stays current with technology innovation.

• Team Mentorship & Capability Building:

Mentor junior and senior analysts, define best practices, and strengthen analytics
capabilities within the team.

Skills & Qualifications

• Education: Bachelor’s degree in engineering, Computer Science, Information Systems,

Supply Chain, Analytics or a related field.

• Experience: 6–8 years in BI, Data Engineering, Analytics (preferably in supply chain /

logistics / retail domain)

• Technical Skills:

o Strong experience with SQL and Snowflake or similar cloud data platforms
o Strong experience in data modeling, dimensional modeling, and semantic layer design. o Hands-on experience with BI platforms (MicroStrategy preferred) and enterprise analytics environments. o Experience implementing data governance, metadata management, and data quality frameworks. o Experience with data architecture and pipeline design. o Experience in Python (Pandas, basic scripting, Flask, FastAPI) or any programming language for data handling. o Experience with low-code/no-code tools like Sigma, Streamlit, or Power Apps. o Experience with emerging trends, best practices, and technologies in analytics, data science, and generative AI

• Computer Science Fundamentals: Best in class foundation in Computer Science

fundamentals, including data structures, algorithms, system design, operating systems,
networking, and software architecture principles. o Strong programming fundamentals, object oriented programming, and algorithms o Strong experience with data modeling principles and relational database theory & normalization techniques. Experience with dimensional modeling, fact/dimension styles and preserving historical context o Ability to apply data structures and appropriate usage (arrays, lists, dictionaries, trees) o Software engineering concepts such as version control, modularity and reuse, testing concepts and code readability and documentation o Experience with leveraging and creating APIs, JSON structures and authentication basics o Deep understanding of software engineering best practices such as clean code, design patterns, testing strategies, performance optimization, scalability, and reliability.

• Analytical Skills:

o Ability to work with large datasets to identify trends, patterns, and anomalies.
o Strong attention to detail with a focus on data accuracy and quality. o Advanced understanding of KPIs and metrics, preferably in a supply chain or operations context. o Statistical knowledge such as descriptive statistics, sampling & distributions, correlation vs causation, A/B testing and anomaly detection basics.

• Leadership & Strategy:

o Experience in stakeholder management and collaboration with enterprise IT
organizations. o Ability to translate business problems into scalable data solutions. o Strong ownership and decision-making capability.

• Business & Functional Understanding:

o Exposure to supply chain, manufacturing, or retail operations.
o Understanding of Enterprise systems such as SAP or similar platforms. o Exposure Retail Planning, Product Development, Order Management, Sourcing / Procurement, Inventory, Warehousing preferred

• Soft Skills:

o Good problem-solving and logical thinking ability.
o Strong communication skills to collaborate with cross-functional teams. o Ability to manage tasks independently and follow structured processes (SOP-driven environment) o Willingness to learn and adapt in a fast-paced, data-driven environment.

• Agentic AI & Advanced Automation Skills:

o Familiarity with agentic AI concepts, including AI systems that can autonomously
plan, execute multi‑step tasks, and interact with tools or data sources. o Experience using AI copilots or agents (e.g., Microsoft Copilot, OpenAI-based agents, workspace automation tools) to streamline analysis, documentation, or workflow orchestration. o Understanding of how AI agents integrate with ecosystems, such as connecting to SQL warehouses, APIs, MCP, RAG, A2A or analytics tools to retrieve, transform, or explain data or automate workflows. o Experience evaluating AI-generated insights, including validating outputs for accuracy, bias, completeness, and alignment with business logic. o Interest in exploring emerging AI capabilities (e.g., autonomous reporting agents, automated data quality checks, or self-service analytics through conversational interfaces).