. Developed and optimized data analytics workflows using Java, Scala, and Python, which resulted in a 40% reduction in processing time for large-scale data sets, enhancing the overall efficiency and scalability of data analytics pipelines within a Big Data ecosystem..Designed and implemented serverless data pipelines using AWS Lambda, AWS DynamoDB, and AWS S3, leading to a 30% increase in data processing speed and a 25% reduction in operational costs by leveraging AWS Step Functions for efficient workflow orchestration..Implemented robust data governance and compliance measures using AWS CloudTrail and CloudWatch, ensuring 100% adherence to regulatory standards and improving the traceability of data engineering processes, which reduced compliance-related incidents by 50%.

. Configured Flume to extract data from web server output files and load it into HDFS, then created partitioned external tables using Hive, AWS Athena, and Redshift, leading to a 30% reduction in data processing time and improving query performance by 25%..Developed and deployed Spark structured streaming to process data from Kafka in real-time and batch modes, implementing CDCs and loading data into Hive. This reduced data latency by 40% and improved the timeliness of data availability for analysis..Migrated an in-house database to AWS Cloud and designed fault-tolerant, auto-scaling architectures using EC2, RDS, and AWS CloudFormation, achieving 99.9% system uptime and reducing infrastructure costs by 20% through optimized resource allocation.

.Engineered RESTful APIs in Python to enable seamless interaction with Neo4j databases, resulting in a 30% reduction in data retrieval time and a 25% increase in application efficiency for downstream processes.*.Implemented comprehensive data governance and security measures across AWS services, optimizing resource allocation and reducing operational costs by 40%. Conducted performance tuning of Redshift, Glue, and Lambda, improving data processing speeds by 50%.*.Designed and deployed data lake solutions using AWS S3 and AWS Glue, facilitating the management of over 10TB of structured and unstructured data. Implemented real-time monitoring with AWS CloudWatch, leading to a 35% reduction in system downtime and proactive issue resolution.*