Gargi Kulkarni

I’m a Data Engineer with over 3 years of experience designing, building, and optimizing scalable data pipelines and cloud-based architectures. My expertise spans AWS, Azure, and Databricks, where I’ve worked on end-to-end data solutions — from ingestion and transformation to analytics and machine learning enablement. I’m passionate about turning raw, complex data into actionable insights that drive smarter business decisions. At CVS Health and previously with Infosys and LTI Mindtree, I’ve developed and maintained data pipelines processing terabytes of information from diverse sources such as APIs, databases, and streaming platforms. Using tools like Airflow, AWS Glue, and PySpark, I’ve automated workflows, improved data reliability, and cut processing time for large-scale analytics operations. My work in optimizing Snowflake and Azure Synapse environments has led to significant cost savings and faster query performance for enterprise reporting and machine learning use cases. I thrive in environments that challenge me to design efficient, high-performing data systems. My technical strengths include Python, SQL, PySpark, and DBT, coupled with strong hands-on experience in AWS services (S3, Redshift, Kinesis, Lambda) and Azure tools (Data Factory, Databricks, Synapse). I enjoy working cross-functionally with data scientists, analysts, and business leaders to ensure data is accessible, reliable, and ready for insight generation. Beyond the technical side, I’m driven by curiosity and a commitment to continuous learning. Whether it’s exploring new data modeling techniques, optimizing performance across cloud environments, or leveraging automation to reduce manual workloads, I’m always eager to innovate. My goal is to contribute to data-driven organizations that value both technical precision and creative problem-solving in their pursuit of scalable, intelligent data ecosystems.