Job Description

Job Summary

Experience

• 17–23 years of overall experience in Data Engineering, Analytics, or Cloud Platforms.
• 10+ years of customer-facing leadership experience in enterprise environments.
• Proven experience in handling teams of 100+ people.
• Experience working in global delivery models, including offshore teams.

Technical Leadership & Architecture

• Provide end-to-end technical leadership for enterprise-scale data platforms across GCP and Azure Data ecosystems.
• Architect and govern modern data platforms including Lakehouse, Data Mesh, Data Fabric, and advanced analytics architectures.
• Lead solution design using technologies such as:
• Drive adoption of best practices in Data Engineering, DataOps, MLOps, security, and governance.
• Review and approve architecture designs, ensuring scalability, performance, cost optimization, and compliance.

People & Organization Leadership

• Build, lead, and scale large teams of 100+ data engineers, architects, and leads across onshore/offshore models.
• Establish strong execution culture with focus on quality, velocity, and innovation.
• Mentor senior technical leaders and create succession pipelines.

Communication & Leadership (Must Have)

• Excellent communication and executive presentation skills.
• Ability to articulate complex technical concepts to non-technical stakeholders.
• Strong written and verbal communication for proposals, presentations, and strategy documents.

Key Responsibilities

Communication & Leadership (Must Have)

• Excellent communication and executive presentation skills.
• Ability to articulate complex technical concepts to non-technical stakeholders.
• Strong written and verbal communication for proposals, presentations, and strategy documents.

Skill Requirements

• Provide end-to-end technical leadership for enterprise-scale data platforms across GCP and Azure Data ecosystems.
• Architect and govern modern data platforms including Lakehouse, Data Mesh, Data Fabric, and advanced analytics architectures.
• Lead solution design using technologies such as:
• Drive adoption of best practices in Data Engineering, DataOps, MLOps, security, and governance.
• Review and approve architecture designs, ensuring scalability, performance, cost optimization, and compliance.

Other Requirements

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