The global trade generated a huge amount of $33 trillion in 2024, as per the United Nations Conference on Trade and Development (UNCTAD). Yet, it is observed that businesses suffer losses worth billions due to unorganised data, wasted marketing dollars and slow decision making. Stores and brands worldwide, face struggles related to the fast-changing trends, which leaves customers with fewer choices and higher prices. Supriya Gandhari, a Senior Analyst Engineer is helping bring a developmental change in the global retail segment, through her inspiring work on Resource Allocation Project. With more than five years of experience in building data systems, she smartly and tactfully uses advanced tools and pipelines, leading to a worldwide shift and befitting retailers, businesses and people everywhere in unexpected ways.
The retail industry is not always smoothly sailing, with an all time major issue of “data silos”. This refers to the information locked in different systems that don’t communicate to each other. This problem makes it difficult for the teams to identify what is working or wasting capital. Another problem is slow reporting, where decisions that require days or weeks might be inaccurate, especially during rapid sales increase or trends changing overnight. Additionally, inconsistent Key Performance Indicators (KPIs), where one team calculates success differently from another team, results in a confusing scenario. Across the world, these challenges cost the industry at large, as for consumers, this relates to high costs of goods and services, and less personalised shopping experiences. From society’s point of view, it slows down the efforts to create a greener and secure retail segment. Supriya, noticing these problem areas, decided to devise solutions for them with her deep knowledge of data engineering.
Initiating with a bold step to this situation, Supriya led the charge on the Resource Allocation Project. Gathering data from marketing budgets, coupon redemptions and sales figures, she pulled together everything into one clear system, employing tools like Snowflake and Azure Data Factory. Her innovative approach was building flexible data models with star and snowflake schemas, just like a well-organised plan bringing clarity to complex data. These models were instrumental in two big successes, Marketing Mix Model (MMM) that helped to track campaign’s performance, quantifying ROI with 95% accuracy across 200+ campaigns and real-time dashboards that showed leaders what was happening at present. With her experience in managing large-scale data processing, Supriya deployed these pipelines over Kubernetes clusters. She brought to use Dagster for smooth data arrangement, while Python tools like Pandas and NumPy assisted in deep analysis.
For better stability, she implemented data checks and testing, alongside a CI/CD system with Azure DevOps and Git. This confirmed data reliability on every step, an expertise gained from her past work lowering data processing times by 40% with AWS Glue and PySpark. She also added data from systems like SAP and commercial P&L reports, changing it from enterprise reporting in a Snowflake data lake, just like her earlier success in datasets migration from Hadoop to Azure.
Beyond her technical contributions, Supriya’s leadership extends to mentoring new team members at Kraft Heinz on dbt modeling, version control and data quality testing. These efforts have resulted in enhanced team productivity by 30% through hands-on training and knowledge-sharing. Her mentorship has developed a culture of continuous learning, allowing her team to independently manage difficult data pipelines. Also, she has advocated best practices in Snowflake optimisation and data modelling, inculcating a data-literate culture across the organization.
Hinting at the world over ripple effect her approaches have begun, Supriya says, “When data flows smoothly and truthfully, it opens doors for smarter choices that reach far beyond one business.”
Her project has influenced retail businesses across the globe, guiding where marketing dollars work best, her system helping shift millions towards high-impact areas, cutting wastage and amplifying results. In Europe, Asia and beyond, this blueprint is now serving as a model for others, with Kraft Heinz extending the framework to support additional regions and product categories.
Supriya’s experience leading data engineering teams, and her collaboration with cross-functional groups in sprint planning and retrospectives, were key elements to the project’s effective scaling. Her methods have simplified data processes at Kraft Heinz, lessening data processing times by 40% and lowering operational costs by 40% through efficient pipeline optimization. This is in addition to improving decision-making speed by 25% across marketing analytics.
The commercial payoff for this is really big with stores and suppliers syncing up like never before. This was possible purely because of clear data that showed what sells and what doesn’t. This has resulted in better stocks on shelves with reduced markdowns, keeping prices steady for consumers. Her methods are helping retailers globally handle the big supply chain mess by placing products where they are needed the most. In busy markets from New York to Nairobi, her work is making trade practices effective and fortifying partnerships, showing her ability to document solutions and onboard new talent to sustain growth.
Through years of optimising Apache Airflow DAGs and lowering manual intervention by 60%, her data pipelines cut through the noise, allowing businesses to understand the trends faster and adjust their plans accordingly. This lets more money be saved on ads that flop, while more getting invested in what connects with the customers. Retailers benefiting too with online shops offering personalised deals, and offline stores keeping shelves stocked with what customers want. This efficiency is boosting sales and building trust between brands and shoppers across nations.
Also by optimising marketing spend, her work reduces both financial and environmental waste. Meeting the growing demands for sustainable choices, companies can focus on eco-friendly initiatives. In developing nations, her data tools have helped local stores reach new customers, lifting small economies. Supriya’s past success in developing streaming analytics platforms with Kafka and Google Big Query for real-time KPI monitoring, demonstrates how she is capable enough to make these global impacts.
Sharing her passion for making a difference, Supriya says, “Watching data convert into real-world benefits, like affordable groceries or greener options, keeps me going.”
Behind her notable works, Supriya’s real power lies in her hands-on approach. By using Snowflake, she developed a system that managed huge data loads, while Azure Data Factory helped in everything to flow smoothly. The CI/CD setup she integrated with Azure DevOps resulted in updates happening quickly and securely, an idea she mastered while migrating high-volume data to Amazon RDS. This cut costs by 40% and enhanced query performance by 50%. Her teamwork with IT and business stakeholders, a skill she worked on during managing quarterly OKRs, refined kinks and sped up results. This technical expertise handles the industry’s major issues with assurance. Data silos were gone with her unified system, slow reports were cut down to hours, and inconsistent KPIs were aligned with Supriya’s clear models. May it be a busy market in India or a quiet town in Europe, her influence is spread worldwide, shaping how goods move and money flows. As more companies adopt her methods, the retail sector gets brighter, greener and more connected. Supriya’s solutions clearly show how a person’s