Manager Data Engineering

Purpose

This position owns the lifecycle of AI/ML use cases development and holds exceptional significance within organization as it develops and maintains AI/ML use cases i.e., consumer segmentation, default prediction, and theft analysis etc. for data-driven decision-making.

This position focuses on data sourcing and model design to deployment including data pipelines, feature engineering, and model lifecycle management using modern tools and frameworks such as Python, TensorFlow, and Databricks, ensuring optimal performance, accuracy, and resilience. The main purpose of the role is to build scalable machine learning and predictive analytics solutions that enable data-driven decision-making and automation across business domains. It also manages the architecture, data integrity, compliance with security, regulatory standards, translates strategic objectives into actionable AI solutions, integrating models with enterprise systems for real-time and batch analytics.

Any lapse in the AI/ML data ecosystem can undermine K-Electric’s ability to generate reliable insights, leading to slower decision-making, and inefficiencies across operations. 

This role contributes to building a secure, reliable, and future-ready AI/ML based data Lakehouse that strengthens decision-making, operational efficiency, and K-Electric’s digital transformation journey.

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
  
  

Functional Competencies

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.