Mentorship and Leadership in Data Engineering Teams: Fostering Growth and Knowledge Sharing

Jagadeesh Thiruveedula¹ & Dr T. Aswini²

¹Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh 533003 India

jagadeeshthiruveedula77@gmail.com

²Department of Computer Science and Engineering
Koneru Lakshmaiah Education Foundation , Vadeshawaram, A.P., India
aswini.oleti@gmail.com

Abstract

The fast-paced field of data engineering demands adaptive leadership and strong mentorship to enable growth and knowledge sharing. As organizations move toward data-driven decision-making, the leadership function in data engineering teams has gained more importance. Transformational and servant leadership styles, for example, are demonstrated to impact team innovation, motivation, and autonomy. Formal and informal mentorship are crucial for the professional growth of data engineers, allowing the exchange of technical and soft skills, as well as improving team performance and cohesiveness. While there have been major leaps in the knowledge of leadership and mentorship, research on how these elements uniquely interact in data engineering teams is limited. This review aggregates evidence from 2015 to 2024 and highlights the most important aspects of leadership and mentorship that foster growth and collaboration among data engineering teams. It underscores the significance of emotional intelligence, effective communication, and peer mentorship in building a culture of knowledge sharing. In addition, as data engineering teams continue to embrace agile methodologies and work in distributed teams, leaders need to shift their strategies to keep the team cohesive and learning continuously. The research gap exists in the absence of holistic studies that investigate leadership and mentorship within the context of emerging technologies like artificial intelligence and machine learning. Moreover, the effect of diversity and inclusion practices on team performance and knowledge sharing has not been adequately examined. This review calls for additional research to fill in these gaps and offer actionable recommendations for organizations that are attempting to optimize the performance of their data engineering teams.

Keywords

Leadership, Mentorship, Data Engineering Teams, Knowledge Sharing, Emotional Intelligence, Career Development, Peer Mentorship, Agile Environments, Emerging Technologies, Diversity and Inclusion, Team Collaboration, Innovation, Technical Growth, Organizational Success.

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• Goel, P. & Singh, S. P. (2009). Method and Process Labor Resource Management System. International Journal of Information Technology, 2(2), 506-512.
• Singh, S. P. & Goel, P. (2010). Method and process to motivate the employee at performance appraisal system. International Journal of Computer Science & Communication, 1(2), 127-130.
• Goel, P. (2012). Assessment of HR development framework. International Research Journal of Management Sociology & Humanities, 3(1), Article A1014348. https://doi.org/10.32804/irjmsh
• Goel, P. (2016). Corporate world and gender discrimination. International Journal of Trends in Commerce and Economics, 3(6). Adhunik Institute of Productivity Management and Research, Ghaziabad.
• Gali, V. K., & Goel, L. (2024). Integrating Oracle Cloud financial modules with legacy systems: A strategic approach. International Journal for Research in Management and Pharmacy, 13(12), 45. Resagate Global-IJRMP. https://www.ijrmp.org
• Abhishek Das, Sivaprasad Nadukuru, Saurabh Ashwini Kumar Dave, Om Goel, Prof. (Dr.) Arpit Jain, & Dr. Lalit Kumar. (2024). “Optimizing Multi-Tenant DAG Execution Systems for High-Throughput Inference.” Darpan International Research Analysis, 12(3), 1007–1036. https://doi.org/10.36676/dira.v12.i3.139.
• Yadav, N., Prasad, R. V., Kyadasu, R., Goel, O., Jain, A., & Vashishtha, S. (2024). Role of SAP Order Management in Managing Backorders in High-Tech Industries. Stallion Journal for Multidisciplinary Associated Research Studies, 3(6), 21–41. https://doi.org/10.55544/sjmars.3.6.2.
• Nagender Yadav, Satish Krishnamurthy, Shachi Ghanshyam Sayata, Dr. S P Singh, Shalu Jain, Raghav Agarwal. (2024). SAP Billing Archiving in High-Tech Industries: Compliance and Efficiency. Iconic Research And Engineering Journals, 8(4), 674–705.
• Ayyagari, Yuktha, Punit Goel, Niharika Singh, and Lalit Kumar. (2024). Circular Economy in Action: Case Studies and Emerging Opportunities. International Journal of Research in Humanities & Social Sciences, 12(3), 37. ISSN (Print): 2347-5404, ISSN (Online): 2320-771X. RET Academy for International Journals of Multidisciplinary Research (RAIJMR). Available at: raijmr.com.
• Gupta, Hari, and Vanitha Sivasankaran Balasubramaniam. (2024). Automation in DevOps: Implementing On-Call and Monitoring Processes for High Availability. International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET), 12(12), 1. Retrieved from http://www.ijrmeet.org.
• Gupta, H., & Goel, O. (2024). Scaling Machine Learning Pipelines in Cloud Infrastructures Using Kubernetes and Flyte. Journal of Quantum Science and Technology (JQST), 1(4), Nov(394–416). Retrieved from https://jqst.org/index.php/j/article/view/135.
• Gupta, Hari, Dr. Neeraj Saxena. (2024). Leveraging Machine Learning for Real-Time Pricing and Yield Optimization in Commerce. International Journal of Research Radicals in Multidisciplinary Fields, 3(2), 501–525. Retrieved from https://www.researchradicals.com/index.php/rr/article/view/144
• Gupta, Hari, Dr. Shruti Saxena. (2024). Building Scalable A/B Testing Infrastructure for High-Traffic Applications: Best Practices. International Journal of Multidisciplinary Innovation and Research Methodology, 3(4), 1–23. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/153.