Keurig Dr Pepper Inc.

o	Working as a senior data engineer in the KDP Tech Data & Analytics team.
o	Leading the finance data product team to build reusable finance data assets in Snowflake, using Informatica Cloud, dbt, and PowerBI, ensuring a single source of data for finance insights.
o	Conducted regular code reviews to ensure high-quality code while mentoring and promoting best practices within team.
o	Developed robust Jenkins CI/CD pipelines integrating dbt testing, Docker image builds, and deployment to GCP Artifact Registry, automating workflows with Automic/UC4 to streamline the deployment process.
o	Seamlessly integrated data quality testing with data transformations by leveraging dbt.
o	Led the development of diverse supply chain analytics solutions, improving manufacturing and customer service performance metrics and enhancing inventory forecasting accuracy using Informatica Cloud, Snowflake, and Tableau.
o	Engineered data pipelines to join, filter, and integrate complex finance datasets enabling insights in MicroStrategy (MSTR) using Qlik Replicate, Informatica Cloud, and Snowflake.
o	Designed, developed, and optimized scalable data pipelines to collect, process, and store large volumes of data from diverse sources in Snowflake, ensuring data accuracy and availability for analytics.

Senior Data Engineer

LAVSUN Technologies Inc

●	Led the installation, configuration, and optimization of Hortonworks Hadoop clusters with components such as Spark and Hive.
●	Worked on distributed processing frameworks including Hadoop, Hive, and Spark to process and analyze large datasets.
●	Utilized Spark to process over 20 TB of data stored in HDFS, enabling business insights and decision-making.
●	Leveraged Apache NiFi to design and implement data flows for collecting, processing, and storing data in Hadoop HDFS.
●	Integrated LDAP/AD for security in Hadoop environments, and managing encryption keys, zones, and audit monitoring in HDFS.
●	Regularly backed up enriched data to a remote Hadoop cluster and Amazon S3 using Hadoop DistCp, ensuring disaster recovery.

Data Engineer

Principal Data Analyst at Keurig Dr Pepper

I am a Senior Data Engineer specializing in building scalable, efficient, and high-performance data pipelines to enable faster, data-driven decision-making. 

With expertise in Snowflake, dbt, SQL, and Python, I help organizations transform raw data into actionable insights by designing optimized ELT workflows, ensuring data quality, and automating processes for efficiency.

💡 Key Highlights:

✔ Designed, developed and maintained scalable ELT pipelines, improving data processing speed and analytics performance.
✔ Led data transformation initiatives, enabling self-service analytics for finance & supply chain teams.
✔ Integrated CI/CD automation for dbt models, ensuring seamless deployments with Jenkins & GCP.

I am passionate about bridging engineering and analytics, ensuring businesses have reliable, fast, and well-structured data to make informed decisions. Whether it's optimizing query performance, automating deployments, or modernizing data infrastructure, I thrive on solving complex data challenges.

📩 Let’s Connect!
If you're looking for a data engineer who can accelerate analytics and build scalable data solutions, feel free to reach out!

Uzwal Goud Vaddeboina

WisdomTooths Consulting

Project: Investment Portfolio Risk Assessment
Data: Information on asset classes, individual holdings (stocks, bonds, ETFs), weights within the 
portfolio, Historical price movements, volatility data for individual assets and asset classes, 
Macroeconomic data such as interest rates (data in txt, csv, json formats)
Algorithms: Recurrent Neural Networks, Support Vector Machines, Sentiment Analysis with Naïve 
Bayes, Ensemble Learning with Gradient Boosting, Autoencoders
Cloud Services: Google Colab, Google vertex AI
MLOps Tools: Vertex AI Tuning, Monitoring, Workspaces
Model Evaluation: Confusion Matrix for Accuracy, Precision Recall, F1 score, Loss function, 
Hyperparameter tuning and Dimensionality reduction for faster execution

Stock Trend Prediction with Social Media Sentiment Analysis 
Data: Utilized Twitter's Application Programming Interface (API) to collect tweets relevant to 
specific stocks, top performers of that sector and filtered tweets by keywords such as company 
names, stock tickers, and financial hashtags.
Algorithms: Deep neural network, Recurrent neural networks, LSTM network
Cloud Services: Jupyter Notebooks, TensorFlow, NLTK, Azure ml studio, AKS
Model Evaluation: R-squared, Mean Squared Error (MSE), Root MSE (RMSE),
Business Outcome: Equipped investors and financial analysts with a novel tool to understand 
market trends from informal sources such as Twitter. The sentiment analysis of social media sites 
can be used as one of the factors to understand investor behaviour

Product Manager Artificial Intelligence

Project: Customer Retention Strategy for Online Food Delivery Platform
Data: Customer order history, customer demographics, customer interactions, platform data, 
customers’ order history, vendor name, cuisine, order time, order size.
Algorithms: Random forest, Gradient Boosting with XGBoost, Logistic regression
Cloud Services: Jupyter Notebooks, Data in csv & json files
Model Evaluation: Confusion Matrix for Accuracy, F1 score, Cross-validation help to prevent 
overfitting and ensure the model generalizes well to real-time data.
Business Outcome: Models with 73% accuracy in Customer Churn Rate Prediction and resulted in 
taking proactive measures to retain customers.

Project: Data Analysis for Optimizing Food Delivery Efficiency
Data: Customer orders, Delivery locations, Order time, Items ordered, Information about 
restaurants partnered with the delivery platform, including location, delivery radius, average 
preparation time. Streamlined web and mobile data in JSON, CSV, and XLS formats using SQL and 
Pandas. 
Programming languages & tools: SQL,Python, Tableau, Power BI
Cloud Services: Jupyter Notebooks, Google Colab, Office365 Teams 
Business Outcome: 50% reduction in delivery time and increased customer satisfaction during peak 
hours.
Used Python to collect data from various sources and convert them into a unified format, Cleaned 
and validate data to ensure accuracy. Exploratory Data Analysis (EDA)Analysed data distribution, 
identify correlations between variables and explore trends based on location, time of day, etc. 
Calculated metrics such as average delivery time, distance travelled per order, and order fulfilment
rate for different regions or restaurants. Identified factors that significantly impact delivery 
efficiency (e.g., peak hours, distance from restaurants).

Data Scientist - Financial services

Project: Chatbot Development using Azure AI for Customer Service (Proof of Concept)
• Developed proof of concepts (PoCs) for Chatbot using Q&A Maker and Luis AI services of 
Azure cloud to support customer service processes.
• Defined the use case for the chatbot, focusing on low-complexity, high-frequency service 
requests to optimize initial implementation.
• Crafted sample conversations and curated a knowledge base using Q&A Maker to train the 
chatbot on common customer inquiries.
• Experimented with various score thresholds in Q&A Maker to fine-tune the accuracy and 
effectiveness of the chatbot's responses

Project: Store Sales Analysis and Insights Dashboard in Tableau
Analysed historical sales data (2 years) from a retail chain (CSV format) to identify sales trends, 
customer buying patterns, and opportunities for improvement. Used Python (Pandas) and SQL for 
data cleaning, manipulation, and exploratory data analysis. Identified top-selling products, 
calculated profit margins, and discovered seasonal variations in sales. 
Developed interactive dashboards in Tableau showcasing key insights:
• Product Performance: Bar charts displaying top-selling products by category and profit margin.
• Sales Trends: Line charts with interactive filters analysing monthly, quarterly and annual sales.
• Customer Segmentation: Pie charts to visualize customer demographics (age groups, location) 
and their buying habits.
These analyses helped the sales team make data-driven decisions regarding product placement, 
promotions, and inventory management, resulting in a 10% increase in sales for specific product 
categories.

Data Scientist

Pangeanz

Online learning platform for Foreign languages www.pangeanz.com and Edumegh Elearning

Founder

Cytel

For Drug trials - Phase 1, reported Treatment Emergency Adverse Events and monitored Patient Safety based on clinical data

Project Trainee Intern

ClipLabs Hyderabad

Development and validation of Clinical reports
Pharmacokinetic datasets analysis and visualization
Testing For Drug trials - Phase 1, reported Treatment Emergency Adverse Events and monitored Patient Safety based on clinical data

Clinical SAS Trainee

Bilingual Business Technologist with 7+ years of experience in solving Business problems and designing Digital Products for web and mobile apps
Hands-on knowledge of Generative AI, AI/ML and analytics models in R, Python and SAS for business domains such as Banking, e-commerce, Pharma
Professional experience in developing and implementing Generative AI based solutions using GCP Vertex AI, Azure OpeanAI services, Hugging Face transformers, LangChain, Vectorization, Google Gemini, OpenAI, ChatGPT
Understanding of Gamification principles and self published author of 'Storytelling with Doodles' on Kindle

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The Battle

Uzwal Goud Vaddeboina

Manpreet Kaur

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