Can Enterprise Data Move Faster?—Insights from an Award-winning Data Engineering Manager

Venkatesh Gundu , Senior Manager of Data Engineering & AI Platform at Victoria's Secret & Co., discusses the impact of cloud migration, AI adoption, and cost management on enterprise transformation.

Enterprises aim to have approximately 60% of their infrastructure in the cloud by 2025. This transformation seeks faster data systems, improved automation, and cost-efficient cloud architectures.

Over his 16-year career, Venkatesh Gundu has contributed to significant cost savings and infrastructure efficiency. His work at Victoria's Secret has led to over $5 million in savings and a 30-35% reduction in infrastructure costs. His efforts in AI-driven migration and data integration are now industry benchmarks.

In 2025, Venkatesh received the Cases & Faces International Award for his innovative work in enterprise data transformation. He is also an IEEE Senior Member and actively mentors upcoming data engineers.

Venkatesh has authored peer-reviewed publications and a white paper on scalable architectures for MLOps maturity and data integration. He emphasizes the need for technological leadership with social responsibility.

Venkatesh highlights the importance of orchestrative leadership in managing distributed teams and maintaining operational continuity. He emphasizes the value of cultural adaptation and knowledge sharing among team members.

Balancing innovation with stability requires operating in both evolutionary and revolutionary modes. A robust methodology is crucial for safe migration, as demonstrated by the successful transition from Teradata to Snowflake.

Address emotional attachment to legacy systems. Automate beneficial processes and accept the necessity of human judgment. Maintain dual systems for validation and stakeholder confidence.

Successful migrations require cross-functional committees and clear success criteria. Incremental delivery and prioritizing high-impact workloads build credibility and unlock resources.

Blameless retrospectives and peer mentorship foster a culture of trust and continuous learning. Cross-training prevents single points of failure and encourages a culture of curiosity.

AI governance should be transparent and explainable. Cost optimization should align with environmental stewardship. Inclusive teams enhance product quality and ensure diverse perspectives.

Based on reporting by TechBullion.