Programming & Scripting
Python (NumPy, Pandas, Matplotlib, Seaborn, SciPy, Scikit-learn, TensorFlow, PyTorch, Keras, NLTK, MLflow, Open Refine), R, Java, Scala, SQL (MySQL, PostgreSQL, Oracle, PL/SQL), NoSQL (MongoDB, Cassandra, DynamoDB).
About
Skills
Machine Learning & AI
Supervised & Unsupervised Learning, Deep Learning (CNN, RNN), Natural Language Processing (BERT, spaCy, GPT-4, Claude), Time Series Forecasting, Feature Engineering, Anomaly Detection, Ensemble Methods (XGBoost, LightGBM, Random Forest), Reinforcement Learning, Generative Models (LLMs incl. LLaMA 2), Prompt Engineering, Retrieval-Augmented Generation (RAG).
MLOps & Deployment
Docker, Kubernetes, PyTorch Serve, Flask, FastAPI, Jenkins, Git, CI/CD Pipelines, Terraform, Model Serving (FBLearner, Torch Serve), Serverless (AWS Lambda).
Big Data & Data Engineering
Apache Spark, Hadoop, Hive, Kafka, Airflow, Prefect, EMR, MapReduce, Apache Flink, ETL/ELT Pipelines.
Cloud & Data Warehousing
AWS (S3, Lambda, Redshift, Event Bridge), IBM Cloud, Snowflake, Teradata, Google Cloud Platform (GCP), BigQuery, Dataflow, RDS, Azure, Azure Data Factory, Databricks.
Data Analytics
Machine Learning, Predictive Modeling, Data Mining, Statistics, Statistical Analysis, ANOVA, Regression Analysis, Time-Series Forecasting, Forecast Accuracy, Classification, Cross-validation, Analytical Models, Exploratory Data Analysis (EDA), A/B Testing, Predictive Analytics.
Visualization & BI Tools
Power BI, Tableau, Google Data Studio, Excel, SSRS, Dashboard Design, Data Storytelling.
Data Modeling & Governance
Dimensional Modeling, Star & Snowflake Schema Design, dbt Tests, Data Contracts, Data Lineage (DataHub), Data Quality Checks, Database Design, Schema Versioning.
Tools & Environments
Jupyter Notebook, RStudio, Linux Shell, RESTful APIs, Apache NiFi, ELK Stack.
Work
Denton, TX, US
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Summary
Developed and optimized scalable ETL pipelines and data infrastructure for real-time processing and analytics within AWS and GCP environments.
Highlights
Developed 15 scalable ETL pipelines using Apache Spark, Airflow, and Python, processing over 50M daily transactions with 99.9% uptime.
Refactored core dbt models and modular SQL logic, enhancing maintainability and reducing month-end close processes by 3 days.
Optimized Amazon Redshift performance by redesigning schemas and partition strategies, boosting query speeds by 30%.
Architected cross-cloud ingestion frameworks across AWS (S3, Lambda) and GCP (BigQuery, Dataflow), reducing data latency by 35%.
Integrated CI/CD pipelines with GitHub Actions and dbt test automation, enhancing reliability and reducing downstream data defects by 25%.
Enabled analytics-ready outputs and data lineage tracking with DataHub, directly aiding a $2M budget variance investigation.
Hyderabad, Telangana, India
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Summary
Performed data analysis and dashboard design to uncover insights and improve reporting efficiency for marketing and product growth.
Highlights
Analyzed over 100K rows of data using SQL, Python, and Power BI, uncovering key insights for marketing and product growth.
Designed 15+ dynamic Tableau dashboards, reducing manual reporting workload by 35%.
Built segmentation logic with Python, scikit-learn, and schedulers, saving 20+ hours monthly in reporting.
Improved accuracy with custom ETL cleansing logic, increasing cross-team report precision by over 20%.
Delivered actionable insights through SQL joins, window functions, and dashboards, reducing failed transactions by nearly 10%.
Hyderabad, Telangana, India
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Summary
Processed data and developed interactive dashboards to improve analytics workflows and identify cost-saving opportunities.
Highlights
Processed over 50K records using Python, SQL, and Jupyter, enhancing analytics workflows and operational reporting.
Built 10+ interactive Tableau dashboards, increasing data visibility and reducing decision-making time by over 25%.
Engineered data cleaning flows in OpenRefine, NumPy, and Python, cutting data preparation time by 40%.
Wrote efficient queries in Snowflake and MySQL, accelerating business dashboard updates by nearly 30%.
Automated recurring ETL tasks with Python scripts, eliminating 40% of manual data preparation time and enhancing workflow integrity.
Conducted regression analyses to uncover cost-saving opportunities in vendor contracts, contributing to strategic solutions.