Dynamic technology leader with a strong Big Data, Cloud Computing, and Software Development background. Proven track record of driving innovation and delivering high-impact solutions in fast-paced environments. Proficien…
Dynamic technology leader with a strong Big Data, Cloud Computing, and Software Development background. Proven track record of driving innovation and delivering high-impact solutions in fast-paced environments. Proficient in Agile methodology, AWS, Hadoop ecosystem, Spark-Scala, and Kubernetes. Adept at orchestrating complex migrations, optimizing systems, and architecting robust frameworks. Recognized for leadership, strategic vision, and commitment to achieving exceptional results.
Leveraged knowledge of Agile methodology for project execution.
Orchestrated the migration of Datalake from On-premises to AWS.
Contributed to the migration of Spark and Java jobs to Kubernetes infrastructure.
Architected a framework utilizing Java, Servlet, Spark, Spark-Streaming, Kafka, Postgres, DynamoDB, Shell Script, HDFS, and S3.
Leveraged development tools such as Bitbucket for code versioning, Jules and Spinnaker for Continuous Integration and Continuous Deployment (CICD), and Jira for managing stories and tasks.
Successfully navigated a transition into a new domain while concurrently mastering unfamiliar technologies, including Java Spring, AWS, and Kubernetes.
Proactively pursued self-education to grasp the intricacies of the new domain and underwent rigorous training to acquire proficiency in Java Spring and AWS.
Attained AWS Cloud Practitioner Certification, validating expertise in cloud computing fundamentals and solidifying knowledge in AWS services.
Demonstrated practical application of acquired skills by spearheading the implementation of a Data Lake and orchestrating its seamless migration to the AWS platform, ensuring enhanced scalability and efficiency.
• Data Lake Design and Architecture: Develop and implement a robust and scalable data lake architecture.
• Data Modelling: Design and implement effective data models that align with business requirements and ensure optimal performance.
• Data Governance: Establish and enforce data governance policies, including data quality, security, and compliance standards.
• Metadata Management: Implement metadata management strategies to catalog and document data assets within the data lake, ensuring proper data lineage and accessibility.
• Data Integration and Ingestion: Lead the development of data integration processes to ingest data from various sources into the data lake.
• Scalability and Performance Optimization: Design and implement strategies to scale the data lake infrastructure and optimize performance based on evolving business needs.
• Collaboration and Stakeholder Management: Collaborate with cross-functional teams, including data engineers, analysts, and business stakeholders.
• Technology Evaluation and Adoption: Evaluate and recommend new tools and technologies to enhance the data lake ecosystem.
• Training and Documentation: Assist in developing training content and provide documentation to support the platform.
Utilized Agile practices throughout the project lifecycle.
Crafted and constructed a performance-tracking solution for both healthcare and retail staff.
Employed Lambda architecture to build an ETL framework facilitating real-time and batch data processing.
Enhanced system efficiency through the application of star schema data modelling techniques.
Utilized various tools, including Hive, Sqoop, Spark-Scala, Spark SQL, SQL Plus, Oracle, HBase, Shell script, and HDFS commands.
Managed code versioning with Bitbucket and facilitated build processes with Maven.
Utilized Rally for story creation and Jira for task allocation.
Worked in Agile methodology to streamline project workflows and enhance collaboration among cross-functional teams.
Engineered and constructed a robust Datalake and Master Job to streamline data management processes, including developing Batch and Streaming jobs for efficient data ingestion and processing.
Contributed to optimizing the ingestion framework by integrating JDBC Puddle, a versatile ingestion tool, to enhance performance compared to traditional methods like Sqoop.
Utilized a comprehensive framework comprising Hive, Sqoop, Spark-Scala, Spark-Structured Streaming, Kafka, Hbase, Shell script, and HDFS command to ensure seamless data integration and processing.
Leveraged development tools such as Bitbucket for code versioning, Maven for Build automation, and Jira for efficient task management and story creation.
Utilized the waterfall methodology in project development.
Engineered and implemented a reconciliation system for CDR data at a prominent Indian telecom firm.
The reconciliation module managed to process 8 million records per minute.
Created a Kafka-based ingestion framework to disseminate high-volume data with minimal latency.
Architected a reconciliation tool using Spark and Apache Ignite for near-real-time processing.
Used Agile methodology for project execution.
As an onsite coordinator, collaborated with the second-largest telecom provider in the USA.
Led the implementation of VDCS (Viewer Data Collection System), VDDS (Viewer Data Distribution System), and DAP (Data Access Platform), collectively serving as a Data Lake for storing engineering data.
Designed a comprehensive framework utilizing Kafka, Flume, Hive, Pig, Java MapReduce, Zookeeper, Sqoop, Distcp, Shell script, and Oozie to deliver an end-to-end solution.
Leveraged development tools such as Bitbucket for code versioning, Jira for task and bug management, Maven for code build, and Jenkins for CI/CD processes.
Led teams at Infosys to excel in technical delivery amidst the introduction of unfamiliar technology at client sites.
Organized targeted meetings to enhance team performance and client satisfaction.
Initiated cross-team collaboration among the Development, Testing, and DevOps teams at Infosys, resulting in improved communication, performance, and understanding of data flow processes.
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