Jun 2022 – Jul 2025 · 3 yrs 1 mo · India · Remote

• 𝗦𝘂𝗺𝗺𝗮𝗿𝘆:
• As Engineering Manager at NOICE, I lead the development and optimization of large-scale data platforms. I focus on real-time, low-latency data pipelines while overseeing the migration of infrastructure to the cloud, improving efficiency, reducing operational costs, and enhancing user engagement through machine learning integration. I also drive data strategy initiatives to align technical objectives with business goals and mentor a team of engineers.
• 𝐒𝐤𝐢𝐥𝐥𝐬:
• 𝐃𝐚𝐭𝐚 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬: Cloud migration, real-time data processing
• 𝐂𝐥𝐨𝐮𝐝 𝐓𝐨𝐨𝐥𝐬: Google Cloud Platform (GCP), AWS, Terraform
• 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠: Python, SQL
• 𝐌𝐋/𝐀𝐈: Generative AI, Large Language Models (LLMs), Machine Learning
• 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Security frameworks, data privacy compliance
• 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩: Team management, cross-functional collaboration
• 𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐓𝐨𝐨𝐥𝐬: Tableau, Data Studio
• 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲:
• Managed real-time data platforms, ensuring high throughput and low latency across all data pipelines.
• Led cloud migration to GCP and AWS, cutting pipeline processing times by 40%.
• Collaborated with product and ML teams to develop data-driven features, increasing user engagement.
• Reduced operational costs by 25% by automating data workflows and optimizing infrastructure.
• Implemented advanced machine learning models (including LLMs and Generative AI) to enhance platform performance.
• Designed a new data governance framework that improved compliance and security across the platform.
• Led quarterly data strategy sessions with C-suite executives to align data projects with business objectives.

Oct 2021 – Jun 2022 · 8 mos · Chicago, Illinois, United States · Hybrid

• 𝐒𝐮𝐦𝐦𝐚𝐫𝐲:
• As Senior Data Engineer at KAR Global, I was responsible for migrating and optimizing our data warehouse systems, leading the integration of Snowflake, refactoring ETL processes, and automating data pipelines. I also worked with AWS services to enhance system efficiency, reduce costs, and support analytics and reporting for the business.
• 𝐒𝐤𝐢𝐥𝐥𝐬:
• 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐢𝐧𝐠: Snowflake, Redshift
• 𝐄𝐓𝐋 & 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Apache Airflow, AWS Glue
• 𝐂𝐥𝐨𝐮𝐝 𝐓𝐨𝐨𝐥𝐬: AWS (MWAA, S3, ECS, EKS)
• 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠: Python, SQL
• 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞 𝐃𝐞𝐬𝐢𝐠𝐧: Centralized data integration
• 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: Workflow automation, Airflow DAGs
• 𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠 & 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Data analysis, Business Intelligence
• 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞:
• Led the migration of data from Redshift to Snowflake, optimizing the data warehouse for better scalability.
• Refactored legacy code and integrated Airflow for more efficient orchestration of ETL jobs.
• Managed AWS services (MWAA, Redshift, S3, ECS, EKS) to improve data processing and reduce costs.
• Automated and streamlined data pipelines with Airflow, saving time and resources on manual tasks.
• Designed a centralized data lake, enabling seamless integration of various data sources across the business.
• Worked with the analytics team to improve reporting systems and ensure data was accessible for reporting.
• Implemented data validation scripts, ensuring the integrity and accuracy of business-critical data.

Sep 2020 – Oct 2021 · 1 yr 1 mo · Chicago, Illinois, United States

• 𝐒𝐮𝐦𝐦𝐚𝐫𝐲:
• At Meta, I worked on enhancing marketing analytics by designing and building ETL processes to handle brand tracker data and KPIs. I automated reporting systems, developed data validation scripts, and created dashboards that provided critical insights for ad campaigns, marketing spend, and brand performance.
• 𝐒𝐤𝐢𝐥𝐥𝐬:
• 𝐄𝐓𝐋 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Spark, APIs, ETL Jobs
• 𝐂𝐥𝐨𝐮𝐝 𝐓𝐨𝐨𝐥𝐬: AWS
• 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Tableau, Data Studio
• 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠: Python, SQL
• 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: Data validation scripts
• 𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠 & 𝐌𝐞𝐭𝐫𝐢𝐜𝐬: KPI development, Marketing Analytics
• 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞:
• Led the creation of KPIs for brand tracker metrics, providing actionable insights into multiple Facebook products.
• Converted business requirements into technical specifications for building data pipelines and dashboards.
• Built Spark-based ETL jobs to extract and transform data from multiple ad campaign sources.
• Developed automated data validation scripts that improved data integrity and reduced manual errors by 20%.
• Designed and developed dashboards to track key business metrics (Spend vs. Sales, Social Media Performance).
• Enhanced cross-functional data workflows, ensuring accurate and timely reporting for multiple marketing teams.
• Streamlined the process of marketing analytics, providing better and faster insights for decision-making.

Jan 2019 – Aug 2020 · 1 yr 7 mos · Tampa, Florida, United States

• 𝐒𝐮𝐦𝐦𝐚𝐫𝐲:
• In my role as Data Science Engineer at Blue Yonder, I designed and implemented data pipelines using AWS Glue, managed ETL workflows with Apache Airflow, and contributed to demand forecasting models that directly supported business decision-making. I also worked closely with data science teams to ensure the integration of various data sources for real-time analysis.
• 𝐒𝐤𝐢𝐥𝐥𝐬:
• 𝐄𝐓𝐋: AWS Glue, Apache Airflow
• 𝐂𝐥𝐨𝐮𝐝 𝐓𝐨𝐨𝐥𝐬: AWS
• 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Demand forecasting, AWS SageMaker
• 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠: Python, SQL
• 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Centralized data system
• 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: Workflow automation, DAGs
• 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞:
• Built AWS Glue-based data pipelines to streamline data extraction, transformation, and storage in AWS S3.
• Implemented Apache Airflow DAGs for automating ETL processes, improving system reliability.
• Developed demand forecasting models using AWS SageMaker, significantly improving business decision-making.
• Created SQL queries to prepare data for analytics dashboards and machine learning models.
• Worked closely with data scientists to integrate various data sources into centralized analytics systems.
• Automated manual data processing tasks, improving efficiency and reducing errors.
• Collaborated with stakeholders to ensure data solutions aligned with business objectives.

Aug 2018 – Dec 2018 · 4 mos · Tampa/St. Petersburg, Florida Area

• Achievement:
• Developed API to Extract data from EDGAR and cleaned data using NLTK library. Implemented statistical models python libraries pandas, numpy, Scikit-learn, keras, TensorFlow, matplotlib, ggplot.
• Deliverable:
• Extracted 4TB of form 10-K data from EDGAR( Electronic Data Gathering, Analysis, and Retrieval) system and COMPUSTAT from 1996 to 2017.
• Written API for extraction of data using python and stored the data in AWS S3 database.
• Implemented a research paper on Geographic Dispersion and Stock Returns by Garcia and Norli.
• Performed text mining using Natural Language Processing to Extract state names counts and from annual reports on form 10-K for comparison of business of different types of firms
• Analyzed the performance of companies before and after use of data analytics in business.

Aug 2017 – Jul 2018 · 11 mos · Tampa/St. Petersburg, Florida Area

• Achievement:
• Predicted the possibility of lung cancer using “dicom” images for MOFFIT Cancer center by performing Image processing and implementing machine learning models using python libraries such as NumPy, Pandas, matplotlib, ggplot, Tensorflow and Keras.
• Deliverable:
• Engineered algorithms for mining a large dataset using Neural Nets, Random Forest, and CNN. Achieved an accuracy of 90%(using Random Forest) by performing Multiple Instance Classification to detect patient level lung cancer
• Reduced the false positive rate that plagues the current detection technology using machine learning Keras, and TensorFlow
• Used python libraries such as Numpy, Pandas, Scikit-learn, Keras and Tensorflow and created plots using Matplotlib in Python
• Predicted the severity level of psychiatric patients to aid relevant rehabilitation strategies
• Created dataset using NLP cleaning techniques including text processing, tokenizing, regular expressions and sentiment analysis
• Built one class at a time classification approach to classify annotated Psychiatric medical record using Natural Language Processing
• Skills:
• Python, R, Data Structure, Data Warehousing, ETL, Hadoop, Map Reduce, Scikit, Keras, TensorFlow, Numpy, Pandas, GitHub, Agile Methodologies, Machine Learning, Deep Learning (Convolutional Neural Networks, Generative Adversarial Networks), Natural Language Processing, Tableau, R-Shiny, SAS Enterprise Miner, SPSS, Jupyter Notebook

Feb 2015 – Jul 2017 · 2 yrs 5 mos

• Achievement:
• Developed predictive models for customer segmentation based on behaviors and financial forecasting using python. Also, created insightful dashboards in Tableau from the datasets created by cleaning the data.
• Deliverable:
• Built models in python to predict the investment loss reserve dollar amount, achieved >90% out-of-sample accuracy rate by using linear regression, random forest and decision tree classifiers.
• Predicted customer segmentation using RFM (Recency, Frequency and Monetary Value) metrics and score calculations using python.
• Conducted data preparation and descriptive statistics on financial data for machine learning model building using python and R
• Created >30 dashboards every quarter using financial and investment data in Tableau as per business requirements
• Implemented the spark for heavy data to build daily reports consisting highest usage data, bill on daily and weekly basis
• Skills: Data Pipelining, Data Profiling, Data Cleaning, Data Extraction, Data Modeling, Machine Learning, Hadoop, Pig, Hive, SQOOP, pySpark, Python, R, Numpy, Pandas, Scikit-Learn, Neural Network, Linear Regression, Logistic Regression

Dec 2013 – Jan 2015 · 1 yr 1 mo

• Analyzed financial and sales datasets to create optimized data flows and improved the performance by 8 %
• Experienced in all phases of Data warehouse development from Requirements, analysis, design, development, testing and post-production support with the strong depth of knowledge in doing data analysis, data quality and source system analysis
• ETL Data cleansing, integration, and transformation using SAP BW and SSIS: Responsible for managing data from disparate sources
• Created SAP HANA Views and reports in HANA and BW
• Developed and designed reports and dashboards for supporting the business analysis using Business Objects, Crystal Reports, Tableau
• Designed databases, stored procedures, reports, and data input interfaces using SQL Server 2010
• Performed analysis and presented results using SQL, SSIS, MS Access, Excel, and Visual Basic scripts
• Skills:
• ETL, Data Modeling, Data Warehousing, Reporting, SAP HANA Developer, Data Visualization using Tableau, SAP Business Intelligence Developer, SSIS, SAP Business Explorer Developer, SAP Business Object Report Developer, SAP Business Intelligence Support Role