Vishwanath Gogi

About Vishwanath Gogi

I’m a highly skilled Data Engineer with 4+ years of hands-on experience designing and deploying scalable, production-grade data pipelines, cloud-based analytics platforms, and ML-ready infrastructure across healthcare, finance, and technology industries. My expertise lies in building robust ETL/ELT workflows, optimizing big data systems, and enabling real-time analytics using the modern data stack. I’ve consistently delivered results such as: Reduced data latency by 85% by implementing streaming pipelines with Airflow, dbt, and AWS Lambda Improved pipeline reliability by 70% through automated testing, alerting, and CI/CD deployment Cut reporting time by 60% by modernizing legacy systems with Azure Data Factory and Databricks Increased analytics adoption by 3x through self-service dashboards in Power BI and Tableau Technical Proficiencies: Programming & Data Science: Python (pandas, NumPy, scikit-learn, PySpark), SQL, Jupyter Cloud Platforms: AWS (S3, Redshift, Lambda, Glue), Azure (Data Factory, Databricks, Data Lake) Data Engineering Tools: Apache Airflow, dbt, Delta Lake, Spark, Kafka Databases & Warehousing: Snowflake, Redshift, DB2, PostgreSQL Modeling & Analytics: Star/Snowflake schema, feature engineering, model monitoring BI Tools: Power BI, Tableau, Looker I’ve led the architecture and deployment of 100+ high-throughput data pipelines processing 10+ TB of data per day, supporting critical use cases such as predictive analytics, regulatory reporting, NLP-driven search, and automated insights. I bring a unique ability to bridge data engineering with machine learning—enabling real-time decision intelligence, reducing infrastructure cost, and accelerating data accessibility across organizations. If you’re building data-driven systems that demand scale, speed, and precision—I bring the technical depth, leadership, and execution to deliver results.

Experience

1. Data Engineer

   Amplifyher Ventures

   Oct 2023 — Present · NY, US

   Automated an ETL pipeline using AWS Lambda, Python, & Pandas, extracting Excel data from S3 and loading it into AWS RDS, improving data processing efficiency by 43% & reducing manual effort.• Designed and developed dynamic financial dashboards in Power BI, integrating SQL queries with AWS RDS to enable real-time monitoring of $12M+ monthly transactions, improving financial decision-making.• Built and deployed a predictive ML model using Scikit-learn, leveraging feature engineering & machine learning algorithms to assess funding application viability, enhancing approval accuracy & reducing risks.• Optimized AWS cloud infrastructure by implementing cost-saving strategies across EC2, RDS, and S3, reducing operational expenses while maintaining 99.9% system uptime for critical financial operations.• Enhanced data security & regulatory compliance (GDPR, financial standards) by implementing robust encryption & access control within AWS RDS & Lambda functions, ensuring the integrity of sensitive financial data.• Developed a scalable AI-driven financial document processing pipeline using OpenAI & Langchain, achieving 99.9% accuracy in extracting mission-critical financial insights while handling increasing document volumes.• Provided end-to-end production support, troubleshooting data refresh cycles, post-Go-Live issues, & client inquiries, ensuring seamless and uninterrupted service.• Led compliance-driven data analysis in healthcare & financial sectors, documenting ETL mappings & streamlining reporting processes to align with regulatory requirements.Key Impact: Transformed financial data operations by automating workflows, optimizing cloud infrastructure, & enhancing AI-driven analytics, leading to greater efficiency, reduced costs, & improved decision-making.

Education

• University of Maryland Baltimore County

  Master's degree

  2022 — 2024