Manager, Data, AI and Analytics

Staples India; Chennai; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Staples India Business Innovation Hub Private Limited in Chennai, India is seeking a senior data and analytics leader to drive the vision, roadmap, and execution of strategic data initiatives across customer, sales, marketing, marketplace and supply chain domains.

The role partners with business leaders to translate high-value opportunities into scalable data products, leads cross-functional teams, and champions enterprise data governance, quality, and AI-enabled insights to improve customer

Qualifications

• Bachelor's degree in CS, engineering, information systems or related field.
• 10+ years in data & analytics, data engineering or BI.
• 3+ years leading medium to large-scale data programs or teams.
• Strong retail/eCommerce or marketplace domain expertise.
• Hands-on with cloud data platforms (Snowflake/Azure/Databricks).
• Proven ability to influence executives and drive transformation.

Responsibilities

• Own vision, roadmap, and execution of data initiatives across multiple domains.
• Partner with leaders to identify opportunities and translate into data products.
• Lead cross-functional teams delivering data solutions and AI-enabled insights.
• Drive data governance, quality, and adoption of enterprise data assets.
• Ensure business outcomes: customer experience, revenue growth, efficiency, cost optimization.
• Champion data and AI capabilities leveraging cloud platforms and analytics.

Skills

Data strategy

Data governance

Stakeholder management

Leadership

Cross-functional collaboration

Education

Bachelor's degree in Computer Science / Engineering / Information Systems

Tools

Snowflake

Azure

Databricks

Power BI

Tableau

Job description

Staples India Business Innovation Hub Private Limited | Permanent

Key Responsibilities

• Own the vision, roadmap, and execution of strategic data initiatives across Customer, Sales & Services, Marketing, Marketplace and Supply Chain domains.
• Partner with business leaders to identify high-value opportunities and translate them into scalable data products and capabilities.
• Lead cross-functional teams delivering modern data solutions, analytics platforms and AI-enabled insights.
• Drive data governance, quality, and adoption of trusted enterprise data assets.
• Ensure delivery of business outcomes through improved customer experience, revenue growth, operational efficiency and cost optimization.
• Champion modern data and AI capabilities leveraging cloud platforms, advanced analytics and automation.

Requirements

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems or a related field.
• 10+ years of experience in Data & Analytics, Data Engineering or Business Intelligence.
• 3+ years leading medium to large-scale data programs, products or engineering teams.
• Strong retail, eCommerce, marketplace, marketing, or supply chain domain expertise.
• Hands-on experience with any cloud data platforms such as Snowflake, Azure, Databricks or similar modern data engineering architectures.
• Proven ability to influence executive stakeholders and drive business transformation through data.
• Strong communication and stakeholder management skills.

Preferred Qualifications

• Experience leading Enterprise AI, Generative AI and Advanced Analytics initiatives that drive measurable business outcomes across different functions.
• Strong understanding of modern analytics architectures, including Snowflake, Databricks, Azure, Power BI, Tableau, dbt and cloud-native data platforms.
• Experience partnering with executive leadership to define and deliver data, analytics and AI roadmaps aligned to strategic business priorities.
• Demonstrated ability to build data products that support large-scale retail and eCommerce operations, including Customer 360, marketing measurement, marketplace analytics, pricing, inventory optimization, fulfillment and delivery experience.
• Experience establishing enterprise-wide data governance, data quality, master data management, metadata management and data observability practices.