Data Engineer Computer Science

SREESUSHMA VADLAMUDI

Open to Relocate P: +1-510-***-**** *************@*****.*** LinkedIn

PROFESSIONAL SUMMARY

As a data engineer, I bring over three years of solid experience in Python, SQL, and cutting-edge ETL technologies to the table. My expertise lies in crafting and optimizing ETL pipelines that drive efficiency and deliver impactful results. Known for driving efficiency and savings, I bring expertise and a collaborative spirit to every project. Backed by a solid foundation in computer science and a commitment to continuous learning, I'm ready to make a significant impact on your team. TECHNICAL SKILLS

Technologies: Python, Java, Tableau, PowerBI, HTML/CSS, JavaScript, Java, Hadoop, Hive, Spark, NoSql, Snowflake, Apache Kafka, Airflow, Amazon Redshift, Amazon Kinesis, Git, Docker, AWS SageMaker Kubernetes, Databricks, Excel, Selenium, Simple Notification Service

DBMS: MS SQL server, Postgres, Oracle, MySQL, Cosmos DB, BigQuery Cloud: AWS, Azure, GCP

ETL Tools: Azure Data Factory, Informatica Intelligent Cloud Services (IICS), Informatica Power Center(IPC), Informatica Data Quality(IDQ), SSIS

EDUCATION

UNIVERSITY OF NORTH TEXAS M.S in Computer Science

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY B.E in Computer Science WORK EXPERIENCE

Data Engineer Cognizant Solutions Aug 2021 - Aug 2022

● Designed, implemented, and maintained RESTful APIs to support data extraction and processing workflows, enhancing system integration and accessibility.

● Built and deployed scalable microservices architecture on AWS using Kubernetes and Docker, resulting in a 40% increase in system scalability and flexibility.

● Developed and optimized data pipelines with Airflow, PySpark, and SQL, processing over 10 TB of data daily and improving pipeline efficiency by 40%.

● Integrated streaming data pipelines using Kafka, achieving real-time data processing capabilities and reducing latency by 30%.

● Utilized CI/CD pipelines with Jenkins and Git for automated testing and deployment, reducing deployment time by 50%.

● Acted as an in-house data expert, defining and enforcing standards for code quality and timeliness, contributing to the reliability and efficiency of the system.

● Applied Agile development methodologies, using tools like Jira for project management and Azure DevOps for version control, leading to more efficient team collaboration.

Project Intern Cognizant Solutions Feb 2021 - July 2021

● Designed and developed ETL Mappings, Mapplets, and Workflows using Informatica, contributing to a 25% increase in data processing efficiency.

● Prepared implementation documents, resulting in a 30% reduction in deployment time and ensuring seamless transitions between environments.

● Demonstrated expertise in data warehouse management, achieving a 99.5% data accuracy rate.

● Assisted in troubleshooting and resolving data pipeline issues, reducing downtime by 20%.

● Contributed to the adoption of cloud technologies, gaining hands-on experience with AWS services like S3 and Glue.

● Supported full lifecycle deployments, ensuring rigorous testing and adherence to quality standards.

● Implemented data quality processes, reducing data inconsistencies by 35% and enhancing data reliability.

● Utilized Jira and GitHub for project management and version control, enhancing team collaboration and productivity.

● Built streaming pipelines using AWS Kinesis, ensuring real-time data processing and analytics. Data Analyst KPCC Services LLP Feb 2019 - Feb 2021

● Utilized SQL and Excel for data analysis, resulting in a 25% improvement in data accuracy and reliability.

● Employed Power BI to visualize and track KPIs surrounding marketing initiatives, leading to a 20% increase in campaign effectiveness.

● Ensured swift, accurate, and dependable data access using SQL, achieving a 98% data availability rate.

● Conducted data quality checks and validations, identifying and resolving data anomalies, resulting in a 30% improvement in data accuracy.

● Developed Power BI dashboards for ongoing performance monitoring, enhancing visibility into key metrics and driving data-driven decision-making.

● Analyzed large datasets using Python for predictive modeling and machine learning, contributing to a 25% increase in customer engagement.

● Presented findings and recommendations to stakeholders, influencing strategic decision-making processes.

● Leveraged experience with data quality processes to define and implement data quality metrics, improving overall data integrity. PROJECTS

Stock Market Real-Time Data Analysis Using Kafka:

● Deployed EC2 instances for producer-consumer Python scripts, generating real-time data with a sampling rate of 1000 data points per second.

● Stored real-time data in S3, accumulating over 1 TB of data within the first month of deployment. Utilized AWS Glue crawler to catalog over 10 million data records.

● Utilized AWS Glue crawler to catalog over 10 million data records.

● Designed and implemented a Power BI dashboard with over 20 interactive visualizations showcasing stock market trends and patterns.

Batch Data Pipeline Optimization using Airflow, Spark, EMR & Snowflake

● Orchestrated end-to-end batch data pipeline using Apache Airflow, Apache Spark, Amazon EMR, and Snowflake, processing over 10,000 movie reviews daily with Spark classification, resulting in a 30% reduction in processing time compared to previous methods.

● Automated data extraction from OLTP database, loading 1 million+ user purchase records into Snowflake weekly, achieving a 50% increase in data ingestion speed and ensuring real-time analytics availability.

● Merge of movie review classifications and user purchases drives insights into sentiment-driven buying patterns, resulting in a 25% uptick in marketing precision.

YouTube Data Analysis Pipeline on AWS: Building Insights for Content Optimization:

● Developed and implemented a YouTube Data Analysis Pipeline on AWS, leveraging services like Amazon S3, AWS Glue, Amazon Athena, AWS Lambda and Amazon QuickSight.

● Engineered a robust data ingestion process using YouTube Data API, ensuring scheduled and efficient batch data transfer into AWS for analysis, facilitating timely updates of analytics dashboards and reports.

● Developed data processing workflows and analytics applications to extract actionable insights from streaming stock market data, enabling timely decision-making and strategy optimization for investors and traders. CERTIFICATES AND ACHIEVEMENTS

● Google Certified: Data Analytics Specialization.

● Microsoft Certified: Azure Fundamentals.

● Golden Key International Honor Society.

Amazon Web Services (AWS); AWS tools, specifically the following applications: Elastic MapReduce (EMR), Identity and Access Management (IAM), Elastic Compute Cloud (EC2), Simple Storage Service (S3), Relational Database Service (RDS), Lambda, and Autoscaling; relational and non-relational database tools; database server platform sizing, troubleshooting, tuning and disaster recovery; Python; CI/CD tools such as Git; Linux; and Apache Spark

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