Shruti Kapoor

• Working as Senior Lead Data Engineer driving large-scale data and AI initiatives.
• Developed an in-house container insights product previously outsourced, saving the company 90, 000 USD
annually.
• Built GenAI tools using LLMs and ML models to predict logistics and transportation costs with high accuracy for
data-driven financial planning.
• Developed a RAG-based system combining Azure Cognitive Search with internal cost data to deliver precise,
context-aware financial insights.
• Designed and deployed GenAI-powered copilots for finance and logistics teams, reducing manual analysis time
by 70%.
• Integrated GenAI capabilities into Azure Databricks pipelines to support LLM-based forecasting, summaries, and
automated data quality checks.
• Built an internal automated prompt-engineering framework to standardize and optimize prompts across teams
and products.
• Served as SME for cost optimization across multiple finance domain teams.
• Designed a cost comparison product that evaluated estimated vs. actual costs, enabling informed booking
acceptance decisions.
• Developed a finance solution to predict routes and costs aligned with profit and loss accounting lines.
• Used real-time mapping to calculate distance between locations for precise route-level cost estimation.

• Worked as SME in Microsoft Azure for Walgreens client
• Developed spark notebooks using PySpark and PySpark -sql for migrating datastage jobs to databricks
• Trained 40 new hires and experienced teammates to build pipelines in Azure data factory
• Achieved a 40% reduction in working hours by piloting the use of master pipeline in Azure Data Factory
• Developed generic parameterized datasets and inline datasets which gradually resulted in 90% decrease in
datasets count
• Created generic databricks notebook for CDC using PySpark which is used by around 1000 master pipelines.
• Used optimization techniques like more generic pipelines for multiload, generic notebooks, less use of SORT in
azure dataflows, dynamic linked service etc
• Implemented log analytics in Azure to keep track of pipeline names, parameters and dataflow activities

• Worked with clients - Enbridge, Coca-Cola, Unilever, Peugeot Société Anonyme
• Built scalable Azure data pipelines, reducing execution time by 50%.
• Developed end-to-end ADF ETL pipelines integrating Azure SQL, Blob Storage, and Synapse.
• Implemented Spark/Scala applications for large-scale data transformation and analytics.
• Automated workflows and testing, cutting manual effort by 80%.
• Redesigned critical ingestion pipelines processing 300+ TB of data.
• Led a 10-member team, driving a 30% revenue increase and earning two rapid promotions as the youngest Star
Achiever.