Job Purpose To lead the design, development, and delivery of enterprise-grade data, analytics, and AI solutions across multiple parallel projects. The role is responsible for driving technology architecture, mentoring teams, ensuring engineering excellence, and implementing innovative solutions leveraging modern cloud technologies, data platforms, and emerging AI capabilities. The incumbent will act as a techno-functional leader capable of managing delivery commitments while defining scalable enterprise architectures and driving adoption of new technologies across the organization. Duties and Responsibilities A- Minimum required Accountabilities for this role • Lead multiple parallel projects involving Data Engineering, Analytics, Cloud, and AI implementations. • Design scalable and secure enterprise data architectures and solution blueprints. • Drive technical design reviews, code reviews, and architecture governance. • Manage end-to-end project delivery including estimation, planning, execution, and stakeholder communication. • Lead development using SQL, Databricks, Python, Azure Data Factory (ADF), and cloud-native services. • Define data models, integration patterns, and reusable frameworks for enterprise use. • Ensure adherence to security, compliance, scalability, and performance standards. • Manage and mentor a team of 6-8 engineers and support their technical growth. • Collaborate with business teams to convert requirements into scalable technical solutions. • Drive engineering best practices including DevOps, CI/CD, automation, and monitoring. B- Additional Accountabilities pertaining to the role • Evaluate and implement emerging technologies and innovative solutions. • Lead AI/GenAI, Machine Learning, and Intelligent Automation initiatives. • Define technology roadmaps aligned with organizational goals. • Support architecture review boards and technology strategy discussions. • Optimize cloud and platform costs through architecture improvements. • Establish reusable accelerators, frameworks, and data products. • Create proof-of-concepts (POCs) for modern technologies and business use cases. • Drive modernization and migration initiatives from legacy platforms to cloud ecosystems. • Support vendor evaluation and technology selection decisions. Key Decisions / Dimensions • Selection of architecture patterns and technology stacks. • Cloud platform design and implementation approaches. • Data integration and processing methodologies. • Build vs. buy technology recommendations. • Technology governance and engineering standards. • Resource allocation across multiple projects. • AI and automation implementation strategies. • Capacity planning and delivery prioritization. Major Challenges • Managing multiple concurrent projects with competing priorities. • Balancing solution scalability, performance, security, and cost optimization. • Driving technology transformation while ensuring delivery commitments. • Maintaining architecture consistency across diverse business domains. • Keeping pace with rapidly evolving AI, cloud, and data technologies. • Building high-performing technical teams and developing future leaders. • Managing stakeholder expectations across business and technology groups. Required Qualifications and Experience • BE/BTech/MCA/MTech in Computer Science, Information Technology, Data Engineering, or related disciplines. • Professional certifications in Azure, Databricks, Cloud Architecture, AI/ML, or Enterprise Architecture are highly preferred. a) Work Experience • 7-12 years of experience in Data Engineering, Solution Architecture, Analytics, or Enterprise Technology Delivery. • Proven experience leading teams of 6-8 members in an enterprise environment. • Experience managing multiple parallel projects and stakeholders. • Strong expertise in enterprise architecture and solution design. • Hands-on experience with large-scale data platforms and cloud ecosystems. • Experience driving technology transformation and modernization initiatives. • Demonstrated success in delivering complex business-critical solutions. • Strong communication, stakeholder management, and leadership skills. • Experience mentoring technical teams and fostering innovation. • Strong analytical, problem-solving, and decision-making abilities. • Ability to influence architecture decisions across teams and business functions.