Description:As an Abinitio Data Engineer, designed, developed, and maintained robust ETL/ELT processes using Azure Data Factory to manage data pipelines and supported advanced analytics. Utilized Ab Initio in containerized virtual machines on Azure to perform critical ETL tasks, ensuring a cloud-agnostic approach for flexible deployment across various cloud environments. Collaborated with stakeholders to implement scalable data storage solutions, ensuring data governance and security using Azure Purview, AAD, and Key Vault. Containerized data solutions using Ab Initio, Docker, manage deployments with Kubernetes, and automate infrastructure using Terraform and ARM templates.Environment: API, Azure, Azure Analysis Services, Azure Synapse Analytics, Blob, Cassandra, REST API, CI/CD, Data Factory, Delta Lake, ElasticSearch, ETL, HBase, HDFS, Java, Jenkins, Kafka, Kubernetes, Oracle, Azure Monitor, Grafana, Power BI, PySpark, Python, RDBMS, Docker, Microsoft Fabric, Snowflake, Spark, Spark Streaming, SQL, SSIS, Teradata.

Description:In my role as a Ab Initio Data Engineer, developed and managed scalable data pipelines using Ab Initio and AWS services like AWS Data Pipeline, EMR, and Redshift. Worked on multiple projects, utlizing Ab Initio graphs as per the use cases. Integrated data from various sources into AWS's data lake and data warehouse and collaborate with data scientists to deploy machine learning models using Amazon SageMaker. Optimized ETL workflows using AWS Glue, Step Functions, and Lambda, manage Apache Airflow DAGs for efficient pipelines, and support cross-cloud data migration between AWS and Azure.Environment: AbInitio GDE, AWS, Big Query, Cloud SQL, Presto, Cloud Storage, Azure, Apache Airflow, BQ-ML, DataStudio, MySQL, Git, Atlassian, Natural Languages, Windows server, python, shell scripts, IAM Security, Service Data Transfer, VPC Configuration.

Description:As a Data Engineer at Ebix Inc, designed and maintained data pipelines integrating data from manufacturing and ERP systems. Implemented data storage solutions, manage ETL processes, and used Apache Flink for real-time data ingestion. Supported data analytics with Tableau and Power BI and automate infrastructure using Terraform. Created complex SQL queries, Python scripts, and Spark processes for data transformation and cleansing, supporting decision-making and operational efficiency.Environment: AWS, CI/CD, Docker, EC2, ETL, Airflow, Apache, Java, Jenkins, Jira, Kafka, Flink, lake, Lambda, MapReduce, MySQL, Oracle, Python, S3, Scala, Snowflake, Spark, SQL.