Lead - Analytics & AI

Lead – Analytics & AI

About the Employer – Classic Marble Company
Classic Marble Company (CMC) is a Mumbai-headquartered stone and surfaces company. It started operations in 1994 and is recognised as one of India’s largest importers of natural stone, having supplied over 150 million square feet of imported marble in the country. In 2009 it launched KalingaStone, India’s first engineered surface manufacturing brand, alongside a natural stone range that includes marble, granite, onyx, travertine and other exotic stones. KalingaStone makes engineered marble, terrazzo, quartz and porcelain surfaces, and these are exported to more than 60 countries across six continents. The company holds ISO 9001, ISO 14001 and ISO 45001 certifications, and its manufacturing and stockyard operations are based in Silvassa.
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Role: Lead – Analytics & AI
Grade: Senior Manager
Experience: 10–14 Years
Department: IT – Analytics & AI
Reports to: IT Head
Location: Head Office (work from office), with periodic visits to the manufacturing units

Job Purpose
To be the internal owner and delivery lead for the company’s data, analytics and AI agenda. The role turns business problems into prioritised, measurable solutions; owns the data strategy and enterprise data warehouse programme through go-live and adoption; builds AI-enabled prototypes for management approval; and works with implementation partners to deliver them, remaining accountable for testing, business sign-off, production rollout and continuous improvement.

This is a hands-on leadership role. The incumbent personally analyses data, builds and modifies Power BI reports, creates prototypes using AI tools and tests partner deliverables, while implementation partners handle large-scale platform engineering and development. Writing production code is not expected, but the role requires enough technical fluency to challenge partner designs, estimates and results with confidence. The role works alongside the SAP team, which owns SAP functional changes; cybersecurity and plant automation are outside its scope.

Key Responsibilities

Data strategy and programme ownership

• Take over the ongoing data strategy and data warehouse programme and act as the company’s data lead, coordinating business stakeholders, IT, the SAP BI/BO team and implementation partners through to adoption.
• Steer the data strategy assessment (current state, master data, KPI tree and role mapping, integration, lakehouse architecture, visualisation, self-service BI and governance) and accept partner deliverables only when they are complete, accurate for the company and usable without the partner.
• Run weekly reviews and sponsor checkpoints, and convert the approved roadmap into a sequenced implementation backlog with value, effort, cost, risk, owners and success measures, keeping a clear line between assessment and implementation scope.

Data warehouse and platform delivery

• Lead the enterprise data warehouse / lakehouse implementation end-to-end (design, partner build, data validation, go-live and transition to support), starting with SAP S/4HANA and CRM data for sales, inventory, purchase and finance reporting, then extending to other business applications and digital marketing data.
• Run vendor due diligence on warehouse and AI platform proposals: data and IP ownership, exit terms and lock-in, separate pricing of warehouse and application, India hosting with company-held admin access, service levels, and cost as data grows.
• Ensure a sound design: layered data, Power BI-friendly fact and dimension tables, business rules and KPI logic kept in the warehouse rather than inside vendor applications, near-real-time data only where justified, and load controls (reconciliation to source, alerts, audit trail).
• Work with the SAP team on SAP S/4HANA extraction (CDS views, custom Z-tables, SAP-compliant methods).

Business intelligence and KPI management

• Engage Management, Sales, Marketing, Finance & Treasury, Plant Operations, Procurement, Logistics and HR to understand the decisions they need to make, and translate them into agreed KPI definitions, report specifications and acceptance criteria.
• Maintain a single KPI dictionary with business owners, and ensure Power BI and the AI chatbot give the same number for the same question; set up role-based access through Power BI row-level security.
• Rationalise existing reports, create wireframes for priority dashboards, and build or modify Power BI reports personally where required, guiding Power BI analysts and interns as the team grows.

Master data, data quality and governance

• Understand how master and transactional data changes in SAP and connected systems affect the warehouse, KPIs, reports and AI use cases; drive master data mapping and matching across systems.
• Establish governance foundations with business heads as data owners: critical data elements, data quality rules, business glossary, stewardship roles and change control.
• Build privacy and DPDP Act requirements into every design: minimum necessary access, masked sample data and India-based data handling.

AI product discovery and delivery

• Work with business users to uncover the real problem behind a request and judge whether AI, automation, reporting or process redesign is the right answer.
• Build lightweight prototypes and MVPs using approved AI tools, and present them to management with the problem, evidence, options, risks, indicative cost and success measures.
• After approval, drive production delivery with AI/ML and application partners against agreed acceptance criteria, control scope, and track adoption and value after launch, including the AI chatbot on business data and process automation (e.g. accounts payable, purchase bidding).

Customer-facing and marketing applications

• Own end-to-end management of Warranty Management, Loyalty Management, Dashloc, Tango Eye, e-commerce, the corporate websites and other customer-facing applications, covering day-to-day support with partners, enhancements, integrations and new implementations.
• Act as the single point of coordination between business users, IT and vendors; manage incidents to agreed service levels and coordinate integrations with CRM, order management, Gallery and other connected systems.

Techno-commercial PMO, testing and documentation

• Maintain one prioritised register of projects, change requests, defects, risks, decisions, owners, dates and costs, and drive items to closure.
• Review partner estimates, commercials and timelines; negotiate and recommend spend for IT Head approval; escalate material trade-offs early.
• Personally test solutions before stakeholder review; coordinate SIT and UAT, documented business sign-off, go-live, training and hypercare.
• Maintain documentation at every stage (requirements, specifications, data mappings, decision logs, test scenarios, SOPs, user guides) and give concise, proactive management updates.

Main Targets include:

• First month: take over the data strategy and warehouse programme, with one consolidated register of projects, issues and risks.
• Within the programme’s six-month window: data warehouse live for sales, inventory, purchase and finance reporting on Power BI, reconciled to SAP and signed off by business owners, with SAP BW/BO reporting replaced.
• KPI dictionary and governance foundations in place, with named business owners for priority KPIs.
• AI chatbot assessed and, where approved, live, giving the same numbers as Power BI.
• A prioritised pipeline of AI and automation use cases, each prototyped and business-cased before investment.
• Stable customer-facing applications, with service levels met; SPOC between business functions, IT and partners for data, analytics and AI.

Required Skills

• 10–14 years of experience, including 5+ years in BI, data warehousing or analytics delivery.
• At least one end-to-end data warehouse or lakehouse implementation, owned from requirements to go-live.
• Good understanding of SAP S/4HANA (or ECC) data across SD, MM, FI/CO and PP, and how data changes affect reporting.
• Hands-on Power BI and working SQL to validate data and check partner deliverables.
• Strong business analysis and product discovery: workshops, KPI definitions, user stories, acceptance criteria and prioritisation.
• Proven multi-vendor delivery management: commercial review, change control, UAT, sign-off and go-live.
• Practical use of AI tools for analysis and prototyping, with judgement on when AI is not the answer.
• Clear communication with senior leadership; works independently and drives closure.

Good to Have

• Cloud data platforms (e.g. Microsoft Fabric, Databricks, Snowflake, AWS) and SAP BW/BO migration experience.
• Data governance frameworks (e.g. DAMA-DMBOK), business glossaries and critical data elements.
• Customer-facing applications such as loyalty, warranty, e-commerce and websites.
• Manufacturing, building materials, consumer durables or distribution background, preferably in-house.
• BE/B.Tech, MCA or equivalent; MBA an advantage. PMP, CSPO or Power BI/Fabric certifications welcome, not mandatory.

Key Ownership Area:
Business Problem → Discovery → Prototype → Management Approval → Partner Build → Testing & Data Validation → Business Sign-off → Go-live → Adoption → Continuous Improvement