Manager Data Engineering

Education

Bachelor/Master’s degree in Information Technology/ Computer Science

Years of Experience

• Minimum 8+ years of experience in IT
• 6+ years in industry known BI Analytics and data warehouse systems
• 3+ years in data Lakehouse (SAP BDC/ Databricks)
• Integration Experience: 1+ years

Area of Responsibilities

1 Data Lakehouse Platform Management

• Manage the planning, design, and implementation of the enterprise Data Lakehouse, ensuring alignment with business goals, data strategy, and technical requirements.
• Manage governance, security policies, and standards to maintain data integrity, compliance, and controlled access across Lakehouse components.
• Support cross-functional collaboration to ensure platform scalability, optimized performance, and readiness for analytics initiatives.
• Monitor platform usage, performance metrics, and health systems to proactively address risks and drive continuous improvement in data operations.

2 Use Case Development & Delivery

• Partner with business stakeholders to identify and prioritize high-impact analytics and AI/ML use cases aligned with enterprise goals.
• Design, build, and deploy predictive models and advanced analytics solutions that address real business challenges.
• Ensure production readiness of models, including validation, monitoring, and lifecycle management.
• Track and measure business outcomes from deployed use cases to demonstrate tangible value and continuous improvement.

3 Data Sourcing & Integration

• Collaborate with business and IT teams to identify, source, and integrate data from SAP and non-SAP systems into the enterprise Lakehouse.
• Ensure data accuracy, consistency, and completeness to support reliable reporting and advanced analytics.
• Establish and enforce governance standards for data ingestion, validation, and transformation.
• Optimize integration workflows and pipelines to enable scalable, efficient, and compliant data operations.

4 Performance, Monitoring, and Resilience

• Implement monitoring frameworks for data pipelines, jobs, and model operations to ensure stability and reliability.
• Identify and resolve recurring performance issues, driving continuous optimization of data and analytics workflows.
• Conduct regular backup, recovery, and disaster recovery drills to safeguard data assets.
• Ensure compliance with RTO/RPO requirements and strengthen resilience across advanced analytics platforms.

5 Cross-Functional Collaboration

• Coordinate with business units, data scientists, and IT to align data platform initiatives with organizational objectives.
• Share project updates and insights with stakeholders to manage expectations effectively.
• Facilitate cross-functional collaboration to ensure smooth execution of analytics use cases.

6 People Management

• Mentor and develop team members to build technical depth, leadership capability, and succession readiness.
• Set clear performance goals, conduct periodic feedback sessions, and manage appraisals to ensure continuous growth and accountability.