Vikram Nimmakuri

WORK HISTORY

EDUCATION

SKILLS

ABOUT VIKRAM NIMMAKURI

Data Engineer with 6 years of experience in analyzing, wrangling, curating, governing, engineering large data sets and creating robust-reliable predictive models for business problems with deployment and maintained in cloud infrastructure like AWS and Azure. Overall 12 years of IT experience working in different technologies, domains and positions. DATA ENGINEERING:Data Ingestion: Design and develop processes to collect, extract, and ingest data from various sources, such as databases, files, APIs, and streaming data.Data Storage: Design, implement, and manage data storage solutions, such as databases (relational and NoSQL), data warehouses, and data lakes, to store structured and unstructured data efficiently and securely.Data Integration: Develop and maintain ETL (Extract, Transform, Load) pipelines to consolidate, clean, and transform data from multiple sources, ensuring data consistency, integrity, and completeness.Data Quality: Implement data validation, cleaning, and enrichment processes to ensure that the data is accurate, reliable, and suitable for analysis.Data Processing: Design and develop scalable data processing solutions using distributed computing frameworks, such as Apache Spark, Apache Flink, or Hadoop, to handle large volumes of data and support complex analytics tasks.Data Architecture: Design and optimize data models, schemas, and storage systems to support efficient data retrieval, querying, and analysis.Performance Optimization: Monitor, analyze, and optimize the performance of data pipelines and storage systems to ensure high throughput, low latency, and efficient resource utilization.Data Security: Implement security measures, such as encryption, access controls, and data masking, to protect sensitive data and comply with data privacy regulations.Collaboration: Work closely with data scientists, analysts, and other stakeholders to understand their data requirements, develop custom data solutions, and provide support as needed.DATA SCIENCE:Study and transform data science prototypes.Design machine learning systems.Research and implement appropriate ML algorithms and tools.Develop machine learning applications according to requirements.Select appropriate datasets and data representation methods.Run machine learning tests and experiments.Perform statistical analysis and fine-tuning using test results.Train and retrain systems when necessary.Keep abreast of developments in the