I build reliable pipelines, dimensional models, analyses, and dashboards that turn raw operational data into useful decisions. My work connects software engineering and analytics with a responsible path toward AI and ML—starting with governed, observable, high-quality data.
I am pursuing Data Engineer, Analytics Engineer, Data Analyst, BI Analyst, ETL Developer, and related data opportunities in the United States.
| Layer | Technologies and practices |
|---|---|
| Data engineering | Python, SQL, PostgreSQL, Pandas, ETL/ELT, APIs, data modeling, data quality |
| Analytics engineering | dbt, dimensional modeling, semantic metrics, documentation, testing |
| Analysis and BI | Exploratory analysis, reconciliation, KPI reporting, Tableau, Power BI, Excel |
| Cloud and delivery | AWS, Google Cloud, Docker, Kubernetes, CloudFormation, GitHub |
| AI and ML direction | LLM applications, RAG, NLP, semantic search, feature quality, responsible evaluation |
Processed 14.9M NYC Yellow Taxi trips through a Python and PostgreSQL pipeline, modeled a dimensional warehouse, added dbt documentation and tests, and delivered a Tableau decision layer over 14.17M curated records.
Python SQL PostgreSQL dbt Tableau Dimensional Modeling
Built a cloud data-lake workflow using Amazon S3, AWS Glue, PySpark, Parquet, IAM, and CloudFormation, with a documented path toward lakehouse tables, observability, and forecasting.
AWS S3 Glue PySpark CloudFormation Data Lake
Created a dependency-free DataOps quality gate that detects schema, type, nullability, and range failures before unreliable data reaches analytics or model training.
Python Data Quality Data Contracts CI/CD ML Readiness
- MapReduce Text Analytics — word count, bigrams, stop-word filtering, and inverted indexes
- Faculty Web Data Pipeline — responsible collection of public profile data into CSV and JSONL
- Round-Robin Workload Scheduler — tested scheduling metrics for shared batch and compute workloads
- Adaptive Python Games — an interpretable online-learning baseline
- C# Data Foundations — typed streaming metrics and anomaly-rule foundations for .NET services
- Observable batch and streaming pipelines
- Governed lakehouse and semantic-layer architectures
- ML feature quality, drift monitoring, and reproducible evaluation
- RAG and agent workflows grounded in tested enterprise data
- Human-centered AI systems with explicit safety and accountability
These are forward directions, clearly separated from the capabilities already delivered in the repositories above.
- Data Analyst, Tower Auto Group — claims and recovery analytics, reconciliation, dashboard automation, and process improvement
- Data Science & Visualization Intern, App Orchid — LLM and NLP applications, Flask APIs, cloud automation, and enterprise dashboards
- Software Engineer, Capgemini — Java and SQL development, API integrations, and AWS foundations
- M.S. in Computer Science, New York Institute of Technology, 2024
- B.Tech in Electronics & Communication Engineering, Jawaharlal Nehru Technological University
Charlotte, North Carolina, USA