SUMMARY ASWIN RAVICHANDRAN New Jersey, USA • (929) 595-3080 • [email protected] • LinkedIn ● Results-driven Data Engineer with 4 years of experience in managing and analyzing large-scale healthcare and business datasets. Proficient in SQL, Python, and ETL tools, with a proven track record of improving data-driven decision-making, automating processes, and developing predictive models. ● Skilled in data visualization, real-time monitoring, and data warehouse integration using tools such as Power BI, Apache Kafka, Snowflake, and Spark. Adept at collaborating with cross-functional teams to enhance data solutions and drive project success, demonstrating strong analytical, problem-solving, and communication skills. EDUCATION Master of Science in Computer Science, New Jersey Institute of Technology, Newark, NJ, USA Bachelor of Technology in Computer Science, Rajalakshmi Engineering College, Chennai, TN, India PROFESSIONAL EXPERIENCE Aug 2022 – Dec 2023 Aug 2016 – May 2020 Mount Sinai | Data Engineer Mar 2023 – Present ● Conducted in-depth analysis on over 1TB of healthcare and pharmacy data using SQL, Python, and R to identify trends and insights, improving data-driven decision-making processes. ● Utilized SQL, Python (Pandas, NumPy), and ETL tools (Alteryx, Talend) to clean, transform, and load data from multiple sources, ensuring data integrity and accuracy. ● Automated routine data processing tasks with Python scripts, saving over 100 hours of manual work per month and allowing for more focus on complex analysis tasks. ● Implemented advanced data visualization techniques using Power BI, reducing report generation time by 50% and enhancing the clarity of actionable insights for stakeholders. ● Developed and deployed predictive models using scikit-learn and TensorFlow to forecast medication demand and optimize inventory management, resulting in a 15% reduction in medication shortages. ● Worked closely with cross-functional teams including product managers, engineers, and healthcare professionals to develop and enhance data-driven solutions, resulting in a 20% increase in project success rates. ● Implemented real-time data monitoring dashboards using Apache Kafka and Grafana, improving the ability to detect and respond to data anomalies and operational issues promptly. ● Leveraged AWS services such as S3 for data storage and Lambda for serverless computing, improving the scalability and cost-efficiency of data processing workflows. ● Utilized AWS Redshift for data warehousing and analytics, enabling faster query performance and more efficient data analysis. ● Applied OLTP (Online Transaction Processing) systems to ensure efficient handling of transactional data and OLAP (Online Analytical Processing) systems for complex analytical queries, enhancing overall system performance and user experience. ● Designed and managed ETL workflows using Talend, Alteryx, and Spark, optimizing data pipelines and reducing data processing times by 40%. ● Integrated Snowflake data warehouse solutions to streamline data storage, access, and analytics, enhancing overall system performance and scalability. ● Applied dimensional modeling techniques to design and implement star and snowflake schemas for efficient querying and reporting in healthcare analytics. ● Implemented Slowly Changing Dimension (SCD) Type 1 and Type 2 techniques to manage and track changes in dimensional data, ensuring accurate historical data analysis and reporting. Version 1 | Data Engineer Jan 2020 – Jul 2022 ● Designed and implemented scalable big data architectures tailored to business needs, balancing performance requirements with considerations for data volume, velocity, and variety. ● Utilized tools like Elasticsearch and NiFi to enhance data processing and search capabilities. ● Applied in-depth Extract, Transform, Load (ETL) skills to orchestrate the smooth movement of data, optimizing processes for efficiency and maintaining data integrity throughout. Leveraged Apache Oozie for workflow scheduling and orchestration. ● Applied expertise in data modeling to design structures that optimize storage, retrieval, and processing, catering to unique challenges posed by varying data volumes and complexities. ● Performed advanced statistical analysis and machine learning to derive actionable insights from complex datasets, leveraging tools such as Python, R, and TensorFlow. ● Developed predictive models to forecast business metrics, improving decision-making processes and strategic planning. Utilized scikit-learn and Spark MLlib for model building and evaluation. ● Conducted exploratory data analysis (EDA) to uncover patterns, correlations, and anomalies in large datasets, using tools like Jupyter Notebooks and Pandas. ● Implemented natural language processing (NLP) techniques to extract meaningful information from unstructured text data, utilizing libraries such as NLTK and SpaCy. ● Applied encryption and access controls to secure sensitive data in Hadoop clusters, Azure Databricks. Utilized GCP (IAM) and Azure (IAM) for identity and access management, and incorporated Azure Event Hub for real-time data ingestion and processing. ● Conducted performance tuning for ETL processes, resulting in a resource utilization optimization of 20%. ● Implemented caching strategies and query optimizations in Hive and Azure Synapse Analytics, leading to a 30% improvement in analytical query performance. Utilized GCP Cloud Storage and Azure Blob Storage for efficient data storage. ● Established data quality checks and validation processes, reducing data errors by 15%. Utilized Azure Data Factory, GCP Data Fusion, and NiFi for data quality management. ● Implemented monitoring solutions, resulting in a 40% reduction in data quality issues through proactive identification and resolution. Used Azure Monitor and GCP Cloud Monitoring for monitoring and alerting. ● Utilized Snowflake for data warehousing, implementing optimized storage and retrieval solutions to support scalable analytics and reporting. Integrated Elasticsearch for enhanced search functionalities and data retrieval. TECHNICAL SKILLS Methodologies: SDLC, Agile/ Scrum, Waterfall Language & Databases: Python, SQL, R, SCALA, MySQL, MS SQL Server, ETL. Python Packages: Pandas, NumPy, Matplotlib, SciPy, Scikit-Learn, SeaBorn, PyTorch, ggplot2, Plotly Data Components: HDFS, MapReduce, Hive, HCatalog, HBase, Sqoop, Flume, Kafka, Yarn, Cloudera Manager, Kerberos, Pyspark Airflow, Kafka Snowflake Data Analytics Skills: Data Manipulation, Data Cleaning, Data Visualization, Exploratory Data Analysis, Data Analysis Others: AWS, AZURE(Databricks), NLP, A/B Testing, Hypothesis testing, ETL, Hadoop, Spark, Big Query, Apache Airflow, Tools: Tableau, Power BI, Advanced Excel, Visual Studio, GIT, Jupyter Notebook Version Control: Git, GItHub Operating Systems: Windows, macOS PROJECTS Enterprise Data Warehouse Design ● Architected an efficient Flight Management System data architecture, using SQL Server, Oracle, MySQL, PostgreSQL & Athena with AWS Glue, SSIS, Talend & Alteryx to enhance Business Intelligence capabilities, improving data retrieval times and analytics accuracy via Power BI and Tableau. Cloud-native Application Development ● Designed and implemented cloud applications on AWS, utilizing EC2, S3, Lambda, Auto Scaling, and CloudWatch, resulting in optimized performance, scalability, and a robust monitoring framework that significantly enhanced operational efficiencies. CERTIFICATION  Azure Data Engineer Associate – Microsoft  Architecting with Google Compute Engine - Coursera