Rami Reddy
SR Data Engineer/Modeler
- Role
- Sr Data Engineer at Fyx Fleet Roadside Assistance
- Location
- Edison, NJ, US
- LinkedIn followers
- 500 followers
About Rami Reddy
Proficient Data Engineer with 8+ years of experience in designing and implementing solutions for complex business problems involving all aspects of Database Management Systems, large-scale data warehousing, reporting solutions, data streaming, and real-time analytics.• Worked on Cloud Environments (AWS, Azure, GCP) and has experience in automating, configuring, and deploying instances on Cloud.• Experienced in AWS services like EC2, Amazon S3, ECS, Amazon RDS, VPC, IAM, Lambda, Amazon Elastic Load Balancing, Cloud Front, Auto Scaling, Cloud Watch, and Redshift.• Experience with data transformations utilizing SnowSQL and Python in Snowflake.• Experience with Apache Airflow in creating workflows as directed acyclic graphs (DAGs), visualizing batch and event data and data pipelines in production, and monitoring progress and troubleshooting issues as needed.• Proficient in data ingestion, ETL processes, data integration, and migration using Azure Cloud Services, Azure Data Lake, Data Bricks, Azure Data Factory, Azure Monitoring, Active Directory, Synapse, Key Vault, and SQL Azure.• Experience building data pipelines and computing large volumes using Azure Data Factory.• Very keen on knowing the newer techno stack that Google Cloud Platform (GCP) adds.• Experience providing highly available and fault-tolerant applications utilizing orchestration technologies like Kubernetes on the Google Cloud Platform.• Hands-on experience across Hadoop Ecosystem that includes extensive experience in Big Data technologies like HDFS, MapReduce, YARN, Spark, Sqoop, Hive, Pig, Impala, Oozie, Oozie Coordinator, Zoo-Keeper, Apache Cassandra, HBase.
Experience
Sr Data Engineer
Jan 2022 — Present · Florence, KY, US
Created data frames in SPARK SQL from data in HDFS and performed transformations, analyzed the data, and stored the data in HDFS. Created multiple scripts to automate ETL/ ELT process using PySpark from multiple sources
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