How Big Data Engineering Enables Scalable Solutions For Modern Enterprises

In today’s data driven world businesses turn to modern data architectures to harness data, drive innovation and stay ahead of the competition. Without these solutions organisations are stuck with inefficiencies, data silos and slow decision making. Snowflake, a cloud data platform, changes the way companies manage and use data. Its scalable solutions give data access, performance and collaboration across the enterprise.

Digvijay Waghela, Sr. Data Architect with extensive experience in large scale data migrations and a Judge at Business Intelligence, was instrumental in moving the Customer Care team’s data operations from Vertica to Snowflake. This migration optimised data processes and laid the foundation for future proof analytics. With strategic insight and technical expertise Digvijay led the migration driving operational efficiency and long term business value.

The Strategic Role of Data Architectures in Enterprises

The backbone of any successful enterprise today is its data architecture. As businesses scale and data volumes grow, having a resilient and future-proof platform becomes essential to maintaining a competitive edge. Snowflake has emerged as a preferred solution in this space, offering businesses a powerful way to streamline operations and remain agile.

“Migrating to Snowflake wasn’t just about upgrading our technology stack; it was a strategic decision to future-proof our operations,” says Digvijay Waghela, a senior data leader. “We needed a platform that could scale as the business grows.” His perspective highlights the importance of aligning technology decisions with long-term agility as data needs evolve.

Among its many advantages, Snowflake’s architecture offers automatic scalability, adjusting to increasing workloads without the need for human intervention—an essential feature for businesses that process several terabytes of data daily. The platform also stands out for its seamless integration capabilities, allowing organizations to connect multiple data sources and tools, enabling centralized management with minimal friction. Moreover, Snowflake’s pay-as-you-go pricing model ensures cost efficiency by allowing businesses to pay only for the compute space and storage they actually use. “One of the main benefits of Snowflake’s model is that it keeps costs in check, especially as we expand data operations,” adds Digvijay.

This combination of scalability, seamless integration, and cost control makes Snowflake an attractive option for any business seeking to optimize data operations and prepare for continued growth.

The Data Migration Journey – Scaling the Technicalities

Migrating from one data platform to another is rarely a plug-and-play exercise. The Customer Care team’s transition from Vertica to Snowflake was no exception. Beyond transferring data, the process demanded meticulous project management, business alignment, and collaborative execution to ensure that outcomes met strategic goals.

“AI and machine learning are integral to our data engineering strategy. By integrating them with Snowflake, we can make real-time, data-driven decisions,” says Digvijay, who brings deep insight into the evolving intersection of AI and enterprise data engineering. His contributions to the field are further demonstrated through his recognition as a judge at the Globee Awards for AI.

The Snowflake migration required thoughtful change management to secure buy-in from key stakeholders and ensure a smooth transition. Digvijay, who also has spoken about unlocking the power of data, emphasizes the importance of this phase: “Change management is critical when moving to a platform that will change how we handle data.” Cross-functional collaboration was also key—bringing together data engineers, business analysts, and leadership to migrate over 1,200 tables while aligning the migration to business priorities. Despite the scale, the migration was completed on schedule and without disrupting day-to-day operations. “We followed a strict timeline to minimize business disruption,” Digvijay explains.

Importantly, the migration wasn’t just a technical upgrade—it was a strategic enabler. By improving data accessibility, it helped accelerate decision-making, supported predictive analytics initiatives, and allowed more granular access control across departments.

AI and Data Engineering Integration for Decision Making

As AI and machine learning increasingly shape enterprise decision-making, the integration of these technologies into data engineering workflows has become a strategic imperative. Snowflake’s compatibility with modern tools like DBT (Data Build Tool) has enabled organizations to build efficient data pipelines that support AI models in real time.

Through Snowflake, AI systems can help teams respond to market trends as they happen, such as dynamically adjusting pricing based on demand. Routine tasks that once required human input are now automated, reducing errors and allowing teams to focus on higher-value work. Predictive analytics models, powered by Snowflake’s scalable infrastructure, offer deep insights into customer behavior and operational efficiency—allowing businesses to move from reactive to proactive strategies.

One tangible example of this integration in action is the optimization of customer service workflows. By analyzing large volumes of historical and real-time data, AI models were able to anticipate issues before they arose, resulting in faster response times and improved customer satisfaction.

Snowflake’s auto-scaling capabilities ensure that computational power and storage dynamically adjust to business needs, maintaining consistent performance without manual intervention. Its open architecture also allows integration with emerging tools in the analytics and data engineering ecosystem, giving teams the flexibility to experiment and adopt best-in-class solutions. Additionally, Snowflake’s built-in data sharing capabilities allow companies to collaborate securely and efficiently with partners and stakeholders across ecosystems.

Digvijay Waghela, who also has published research on Data & BI Techniques in the Nanotechnology Perceptions Journal, is considered an expert in this field. Businesses can get the most out of Snowflake and data infrastructure. Snowflake helps businesses meet today’s needs and prepare for tomorrow’s growth and challenges.