I’m a data professional interested in building scalable data pipelines, cloud data platforms, and data quality solutions. My interests span ETL/ELT workflows, distributed data processing with Spark, cloud data warehousing, analytics, and workflow automation. I’m also exploring AI-driven data solutions and CI/CD practices to build reliable, efficient, and maintainable data systems. I’m passionate about continuous learning and applying modern data engineering practices to solve real-world problems.
Data Engineer
OSI Digital
- Multi-Cloud Architecture & Ingestion: Designs unified cloud data platforms that integrate structured and unstructured datasets across multi-cloud ecosystems using fault-tolerant ingestion pipelines, cloud-native orchestration, and distributed event platforms.
- Lakehouse Design & Database Synchronization: Implements structured Medallion lakehouse architectures to ensure data integrity and compliance, alongside sophisticated synchronization logic—such as real-time relational-to-graph database loads—to drive advanced network mapping.
- AI-Driven Data Transformation: Embeds multimodal AI and Large Language Models (LLMs) directly into automated pipelines to transform unstructured media into queryable assets, while leveraging AI-augmented IDE environments to accelerate feature delivery and real-time insights.
Data Engineer
Urgent.ly inc
- Unified Platform & Analytics Integration: Operates a centralized, unified data platform that integrates structured and unstructured datasets from internal and external sources to drive real-time decision-making, predictive analytics, and strategic planning.
- Scalable Pipelines & Distributed Compute: Implements fault-tolerant data pipelines using cloud-native orchestration and robust ETL/ELT processes, leveraging distributed compute engines for large-scale processing, machine learning workloads, and interactive reporting.
- Governance, Security & Automation: Enforces strong data governance, security, and compliance to safeguard asset integrity, utilizing automated workflows and orchestration frameworks to accelerate the delivery of near real-time insights for stakeholders.
Data Engineer
New York State Energy Research and Development Authority
- DEI Analytics & Stakeholder Collaboration: Evaluated demographic data, employee surveys, and internal datasets at NYSERDA to uncover DEI trends and opportunities, collaborating with cross-functional teams to deliver actionable insights aligned with organizational goals.
- Cloud Workflows & Scalable Data Management: Processed and cleaned large-scale datasets using scalable data preparation techniques, managing cloud storage environments and automating data workflows via cloud-native orchestration tools.
- Advanced Analytics & Visual Reporting: Utilized distributed analytics platforms for advanced processing and machine learning model development, creating interactive BI dashboards to communicate clear, data-driven insights to key stakeholders.
Data Engineer
Tata Consultancy Services
- Scalable Architecture & ETL Pipelines: Developed a scalable, multi-client cloud data system using cloud-native integration and distributed processing platforms to drive end-to-end ETL/ELT and real-time analytics for structured and unstructured data.
- Storage, BI & DevOps Automation: Implemented tiered cloud object storage and data warehouses alongside modern BI platforms, supported by a full DevOps CI/CD pipeline for automated deployments, version control, and streamlined release cycles.
- Security, Governance & Performance: Enforced role-based access control, encryption standards, and robust data governance to maintain compliance, delivering cost-efficient, high-performance insights to stakeholders across business units.
F1 Racing Database Analytics
Successfully migrated historical F1 datasets from on-premise SQL Server to Snowflake using Azure Data Factory (ADF), designing an optimized, partitioned data model to seamlessly integrate race, driver, and team data for high-performance query execution. Engineered scalable SQL transformations within Snowflake to process large-scale race history and calculate key performance metrics—such as average lap speeds, pit stop timing, and driver statistics—while publishing dynamic, interactive Power BI Service dashboards equipped with Snowflake role-based access control (RBAC) to enable secure multi-team collaboration and real-time trend analysis.
Predictive Maintenance for IoT Devices
Developed predictive machine learning models in Python using historical and real-time IoT home automation telemetry to forecast device maintenance needs and optimize system reliability. Engineered end-to-end ETL pipelines using Azure Data Factory and Azure Data Lake for multi-source data ingestion and scalable storage, leveraging Azure Databricks for distributed data processing and model training. Designed interactive Power BI dashboards to visualize actionable maintenance alerts and performance trends, supported by automated CI/CD pipelines to streamline deployment cycles for both data workflows and ML models.
Heart Stroke Prediction
Developed and evaluated robust machine learning models—including K-Nearest Neighbors (KNN), Random Forest, XGBoost, and CatBoost—to accurately predict heart stroke occurrences, achieving an impressive 95% accuracy rate in identifying potential healthcare risks. Managed end-to-end data preprocessing, quality assurance, and technical reporting using Python, NumPy, Pandas, and Scikit-learn to ensure data integrity and deliver effective predictive analytics solutions.
Sales Performance Dashboard
Integrated sales data across multiple sources using Azure Data Factory to orchestrate end-to-end ETL workflows, storing and aggregating processed datasets within Azure SQL Database for high-integrity downstream analysis. Utilized Azure Synapse Analytics to perform complex queries and build scalable data models across high-volume transaction data, enabling comprehensive performance tracking. Designed interactive Power BI dashboards featuring dynamic filters, slicers, and drill-down capabilities, backed by rigorous data validation to deliver accurate, actionable sales insights to key stakeholders.
Real Estate Price Prediction
Analyzed raw real estate datasets to identify key predictive features, ensuring high data quality by handling missing values with median imputations and removing statistical outliers via the Z-score method. Scaled and normalized numerical features to optimize data distributions, enhancing machine learning model training stability and overall performance. Implemented and evaluated multiple machine learning algorithms using train-test splits, successfully achieving strong prediction accuracy and validating the effectiveness of the preprocessing workflow.
Master's degree, Data science
University at Albany · 3.48/4.00
Bachelor of Technology, Electronics and Communications Engineering
Anurag Group of Institutions · 8.79/10.00
Generative AI Course With Langchain and Huggingface
Udemy
Jul 2025Snowflake - The Complete Masterclass
Udemy
Oct 2024End to End Azure Data Engineering Real Time Project
Udemy
Sept 2024Microsoft Power BI: The Complete Guide
Udemy
Sept 2024Python programming authorized by University Of Michigan
Coursera
Dec 2020
Attendee · Oct 24, 2026