As businesses embrace data-driven decision-making, the need for scalable, high-performance data architectures has never been greater. Artificial intelligence (AI) and cloud-based data engineering are revolutionizing how enterprises manage large-scale data migration, analytics, and automation, enabling them to enhance agility and efficiency while reducing operational bottlenecks.
At the forefront of this transformation is Digvijay Waghela, an award-winning Data Architect at Chewy. He is known for his work in AWS, Agile methodologies, and enterprise data engineering for over 10 years. As an Associate editor at SARC, and his leadership on the DBT Snowflake Migration project has demonstrated how modern businesses can successfully transition from legacy data warehouses to AI-powered, cloud-native solutions, improving real-time analytics and operational resilience.
Modernizing Data Architecture with AI and Cloud Engineering
As Senior Data Architect, Digvijay played a pivotal role in the design and execution of the DBT Snowflake Migration project, a 10-month initiative from June 2024 to March 2025. His expertise in AI-driven automation, workflow orchestration, and cloud computing helped create a seamless transition to Snowflake, bringing significant advancements to enterprise data management.
By leveraging DBT and Airflow, Digvijay introduced a modular, scalable framework that transformed how businesses manage data workflows. The migration included:
- Rebuilding 172 ETL pipelines using DBT models, allowing for enhanced data lineage tracking and better maintainability.
- Optimizing database structures, reducing redundant tables by ~150, and improving storage efficiency.
- Automating ETL execution with Airflow DAGs, significantly reducing manual intervention and approval times.
- Enhancing security controls by implementing granular, role-based permissions, ensuring efficient governance across data teams.
By integrating Snowflake with AWS-based applications like CB, NICE, Kronos, Sprinklr, and Oracle CRM, Digvijay helped organizations unlock real-time data visibility, improve cross-functional analytics, and accelerate cloud adoption.
Delivering Measurable Business Impact
The DBT Snowflake Migration project provided significant operational and performance enhancements, helping businesses eliminate data silos, improve reporting accuracy, and enhance decision-making speed.
- Performance Optimization:
- ETL job execution delays were eliminated, ensuring instant data availability for analytics teams.
- Dashboard refresh rates improved, leading to faster reporting and insights delivery.
- Troubleshooting time was reduced by 30%, thanks to DBT’s enhanced data lineage tracking capabilities.
- Operational Efficiency Gains:
- 20% reduction in redundant code via modular DBT models.
- 23% faster deployment cycles, as automation streamlined workflow approvals and execution.
- Lower maintenance overhead, freeing up engineering resources for higher-value projects.
- Scalability and Cost Savings:
- Cloud-native compute optimization enabled cost-effective scaling, reducing infrastructure costs.
- Better AWS integration supported data science and AI applications, improving cross-functional analytics.
- Resource allocation improved, allowing DBAs to optimize Snowflake storage and compute usage efficiently.
Digvijay’s data engineering innovations have set a new industry benchmark for cloud-based analytics, proving that AI-powered automation and modular architecture can drive higher efficiency and long-term business growth.
The Future of AI-Driven Data Engineering
As data volumes continue to grow, AI and cloud-native architectures will become even more integral to enterprise IT strategy. Digvijay, whose research on data security and AI-driven cloud solutions has been recognized at ICMR IITM and published on Dzone, predicts that self-optimizing, AI-driven data ecosystems will be the next evolution.
“AI doesn’t just improve data efficiency—it transforms how businesses operate,” Digvijay, who also won the Best Big Data Engineering Research Paper & Presentation at ICMR, explains. “Automated ETL pipelines, predictive data governance, and real-time AI monitoring will redefine enterprise data management.”
The DBT Snowflake Migration project is more than just a technological upgrade—it represents a strategic shift toward AI-powered, scalable data solutions. Businesses that embrace intelligent data architectures today will be the ones leading the digital economy tomorrow.
