I am an aspiring Data Engineer and M.Sc. Information Technology student passionate about designing scalable data solutions and enabling data-driven decision-making. My interests lie in data pipeline development, cloud da…
I am an aspiring Data Engineer and M.Sc. Information Technology student passionate about designing scalable data solutions and enabling data-driven decision-making. My interests lie in data pipeline development, cloud data architecture, and analytics engineering, where I enjoy transforming raw data into actionable insights.
During my internship at Edge Matrix Corporation, I designed and optimized automated ETL pipelines for solar energy trading and risk management. The work involved integrating diverse data sources, ensuring consistency, and building scalable workflows using Cloud Data Fusion, BigQuery, and Looker Studio, with Cloud Composer planned for orchestration and automation.
Beyond my internship, I’ve built end-to-end data warehousing and analytics pipelines for platforms like Netflix Analytics and Financial Market Intelligence using Snowflake, dbt, Airflow, and SQL. These projects strengthened my understanding of data modeling, governance, and workflow automation, emphasizing the importance of data quality, maintainability, and reliability.
I am proficient in Python, SQL, and Java, and have experience working with tools such as GCP, Snowflake, dbt, Airflow, Power BI, and Looker Studio. My goal is to begin my career as a Data Engineer where I can apply my technical foundation, contribute to impactful data projects, and continue growing as part of a forward-thinking team.
Designed and implemented scalable ETL pipelines in GCP to ingest and transform solar trading data from PV meters, TES, and weather APIs.
Built workflows with Cloud Data Fusion, applied transformations, ensured data quality, and loaded into BigQuery while securing sensitive data with masking and validation.
Created Looker Studio dashboards and automated workflows with Cloud Composer to optimize pipeline reliability and reduce downtime.
Troubleshoot and optimize broken pipelines, minimizing data latency and ensuring uninterrupted delivery.
Designed and implemented scalable ETL pipelines in GCP to ingest and transform solar trading data from PV meters, TES, and weather APIs., Built workflows with Cloud Data Fusion, applied transformations, ensured data quality, and loaded into BigQuery while securing sensitive data with masking and validation., Created Looker Studio dashboards and automated workflows with Cloud Composer to optimize pipeline reliability and reduce downtime., Troubleshooted and optimized broken pipelines, minimizing data latency and ensuring uninterrupted delivery.
Developed an end-to-end automated data warehousing and analytics pipeline designed to process, transform, and manage large-scale financial and stock market datasets efficiently. The project emphasized scalability, automation, and data governance, showcasing expertise in modern data engineering tools such as Apache Airflow, dbt, and Snowflake.Designed a scalable ETL architecture that ingests raw financial data from multiple sources into Snowflake, enabling structured and reliable data storage optimized for analytics.Implemented transformation logic using dbt (Data Build Tool), creating modular and reusable SQL models with dependency tracking, version control, and automated testing for data quality and consistency.Configured Airflow DAGs for automated pipeline orchestration, scheduling, and monitoring, ensuring seamless data flow from ingestion to analytics-ready stages with minimal manual intervention.Established data governance and validation frameworks, including schema checks, data freshness validation, and model documentation, to maintain data reliability, traceability, and auditability.Optimized query performance and transformation efficiency by applying incremental materializations, partitioning strategies, and warehouse tuning in Snowflake.Delivered analytics-ready datasets and insights that support decision-making dashboards and financial intelligence reporting, demonstrating the pipeline’s real-world business applicability.This project strengthened my proficiency in ETL pipeline design, workflow automation, and cloud-based data warehousing, while enhancing my understanding of orchestration frameworks, CI/CD for data models, and production-grade analytics infrastructure.
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