Mustakim Sayyad
Development
Maharashtra , India
Skills
Data Engineering
About
Mustakim Sayyad's skills align with System Developers and Analysts (Information and Communication Technology). Mustakim also has skills associated with Database Specialists (Information and Communication Technology). Mustakim Sayyad appears to be a low-to-mid level candidate, with 3 years of experience.
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Work Experience
Big Data Administrator
BRAVEZONE SOFTECH
December 2020 - December 2021
- Achievements/Responsibilities Configured and optimized ETL components, enhancing data processing efficiency and analysis by 16% through fine-tuning HDFS, YARN, Hive, Spark, Kafka, and Sqoop. Executed High Availability measures for Name Node and Resource Manager, achieving 99.9% cluster uptime. Effectively managed user namespace, disk space quotas, and implemented regular snapshots of critical directories and files, reducing data loss risk by 40%. Proficiently performed Commissioning, Decommissioning, Balancing, and Management of Nodes, optimizing cluster performance by 25%. Optimized performance for YARN, Hive, and Spark, achieving a 22% improvement in system performance and faster response times. Designed and implemented backup and disaster recovery strategies for Big Data Clusters, ensuring data integrity and availability, resulting in a 95% data recovery success rate in the event of a disaster. Performed kernel tuning across different Linux distributions and associated services, leading to a 15% enhancement in overall system performance.
Big Data Engineer
BRAVEZONE SOFTECH
January 2022 - Present
- Achievements/Responsibilities Designed and managed data pipelines in Azure Data Factory for Azure Data Lake Storage Gen2, resulting in a 40% increase in data processing effectiveness. Leveraged Azure Databricks to optimize data processing and analytics, reducing processing time by 50%. Improved query execution time by 30% through PySpark optimization techniques like partitioning and bucketing. Engineered Databricks optimization strategies, resulting in a 40% enhancement in query performance. Utilize broadcast join optimizations in Databricks, achieving a 16% improvement in query performance by minimizing data shuffling and optimizing data distribution. Executed data storage optimizations using the vacuum command in Databricks, reducing storage overhead by 30%. Proficient in working with file formats like CSV, Parquet, Avro, and ORC, reducing storage costs by 22%. Spearheaded a major pricing restructure by redirecting focus on consumer willingness to pay instead of product cost; devised a three-tiered pricing model which increased average sale 35% and margin 12%. Enhanced performance by loading processed data into Spark DataFrames, implementing partitioning and bucketing techniques, and selecting efficient file formats such as ORC and Parquet, resulting in an 18% speedup in data processing.
Education
Maharashtra Institute of Technology
B. Tech
January 2018 - January 2021
