Khyati Kotak

About Khyati Kotak

I’m Khyati Kotak, a dedicated Data Engineer with over 6 years of experience in designing and optimizing data pipelines and architectures. Based in New Jersey, I thrive on leveraging my expertise in Python and SQL to develop scalable data solutions that drive analytics and insights. My passion lies in utilizing technologies like Apache Spark, AWS, and Databricks to create efficient data workflows, ensuring data integrity and reliability.With a background as a.NET Developer, I bring a holistic approach to problem-solving, bridging the gap between data engineering and application development. I\'m committed to continuous learning and eager to adopt innovative technologies that support data-driven decision-making. Let’s connect and explore how I can contribute to your team’s success!Key Achievements:• 50% Reduction in Data Processing Time: Streamlined data retrieval processes using automated Databricks workflows.• 10-fold Increase in Data Ingestion Speed: Implemented parallel processing in existing workflows.• Successful Migration to AWS: Led the migration of on-premise applications to AWS, enhancing scalability and performance.• Automated 5 Daily Processes: Increased operational efficiency by 80% through RPA automation at Infosys.• Developed Robust APIs: Crafted RESTful APIs with FastAPI for dynamic financial impact score calculations.• Collaborative Data Engineering: Engineered Databricks workflows for seamless integration of data science and engineering.Feel free to reach out to discuss how we can collaborate!

Experience

1. Senior Data Engineer

   ICIMS

   Jan 2025 — Present · Holmdel, NJ, US

   In my role at iCIMS, I have driven the design and implementation of high-performance data pipelines to enable real-time analytics and streamline enterprise data workflows. I architected solutions that significantly reduced latency, improved throughput, and empowered business users with faster, more reliable insights. My work spans data engineering, automation, and visualization, leveraging modern cloud platforms and tools to deliver measurable business impact.Key Contributions:• 80% Reduction in Data Latency: Architected and implemented real-time pipelines to extract and transform MongoDB data into Amazon S3, enabling near real-time analytics.• 30% Increase in Data Throughput: Designed and optimized ELT workflows with Python and SQL, ensuring seamless integration across PostgreSQL, S3, Redshift, DBT, and Looker.• 90% Process Automation: Orchestrated pipelines with AWS services (S3, Redshift, Lambda, Step Functions), reducing manual intervention and improving reliability.• 25% Improvement in Data Quality: Built modular DBT transformations to enhance data consistency and transparency in analytics models.• 35% Fewer Ad-Hoc Requests: Developed LookML models and dashboards in Looker, empowering stakeholders with self-service reporting.• 50% Faster Query Performance: Wrote and optimized complex SQL queries in Redshift to improve execution speed for large-scale analytical workloads.These achievements highlight my ability to deliver scalable, automated, and business-focused data engineering solutions while leveraging cutting-edge AWS and BI technologies.

Education

• Sigma Institute Of Technology

  Bachelor of Engineering (B.E.), Computer Engineering

  2008 — 2012

• Nirma University

  Master of Technology (M.Tech.), Computer Science and Engineering

  2012 — 2014