Aparna M Email and Phone Number

Developed and maintained robust ETL data pipelines using AWS Data Pipeline and AWS Glue, working with extensive datasets in Amazon S3. Leveraged AWS EMR, Spark, Scala, Python, and Hive to create scalable big data solutions.Facilitated application onboarding by creating stubs for producers and consumers, enhancing integration capabilities. Supported data transformation processes, managing data structures, metadata, dependencies, and workloads across cloud platforms. Built and optimized ELT/ETL pipelines for AWS Redshift using Python and SQL, and designed custom ETL workflows to integrate diverse data sources.Wrote complex SQL queries for Amazon RDS and Redshift, and implemented large-scale data warehouses using Snowflake, optimizing data storage and retrieval for advanced analytics. Designed data migration strategies to Snowflake and configured Snowflake's data sharing features for real-time insights.Created and managed database schemas, tables, views, indexes, and stored procedures, ensuring data integrity. Developed data transformations using AWS Lambda and Glue, and deployed machine learning models with AWS SageMaker. Built full-stack applications using AWS Amplify and API Gateway, and managed containerized applications with AWS ECS and EKS.Collaborated with AWS architects to troubleshoot automation and data pipeline efficiency issues. Coded and optimized AWS Lambda functions for ETL processes, and designed data integration solutions across Hadoop and RDBMS within AWS. Implemented CI/CD pipelines using AWS CodePipeline and Jenkins, and deployed Delta Lake on AWS for data consistency and ACID transactions.Maintained data pipelines using Delta Lake, improving reliability and operational efficiency. Worked closely with DevOps to develop automated CI/CD pipelines, and gained hands-on experience in Python and Scala. Managed Hive scripts and Spark SQL tasks to ensure data integrity and stability in ETL operations.

Experienced Data Engineer specializing in AWS-based solutions for automating data pipelines, ETL processes, and data transformations using AWS Glue and Lambda. Designed and implemented data ingestion and storage solutions with AWS S3, Redshift, and Glue, enhancing data integration and analytics capabilities.Developed ETL workflows with AWS Glue to load data from multiple sources into Redshift. Integrated AWS SNS and SQS for real-time event processing, improving communication efficiency. Utilized AWS Step Functions to orchestrate and monitor complex workflows, ensuring operational integrity. Implemented AWS Athena for ad-hoc data analysis on S3-stored data and used AWS CloudWatch for resource monitoring and performance maintenance.Created real-time data streaming solutions with AWS Kinesis. Managed DNS configurations using AWS Route53 for optimal application deployment. Leveraged Spark JDBC for data extraction from diverse sources and Spark SQL for building complex data frames and performing advanced SQL operations.Conducted advanced data frame operations, including schema management, aggregations, and joins. Established Amazon SNS topics for effective notifications and implemented cross-account messaging with SQS. Integrated Apache Kafka with AWS services for real-time error capture and developed Spark Streaming applications with Scala.Interfaced with databases like PostgreSQL for data retrieval and extraction. Orchestrated Docker containers for consistent application deployment and managed CI/CD processes with Jenkins and AWS CodePipeline. Utilized AWS Glue to deploy and manage Spark jobs on AWS EMR clusters.Developed AWS Lambda functions for server management tasks and executed code snippets efficiently within the AWS cloud. Practiced comprehensive version control with Bitbucket and worked with JSONB data formats for data conversion and storage. Employed Terraform scripts for provisioning AWS resources and led Spark job deployments on EMR clusters.

Data Engineer with extensive experience in leveraging AWS services for big data solutions. Utilized AWS Data Pipeline and AWS Glue to automate data ingestion from MySQL to Amazon S3, transitioning from traditional Sqoop. Performed data aggregations using Apache Spark and Scala within AWS EMR, and stored results in AWS Glue Data Catalog. Managed data lakes with AWS Lake Formation, integrating with EMR and replacing Hadoop environments like Hortonworks and Cloudera.Developed HiveQL queries in AWS EMR for complex data analysis, and managed HBase tables on AWS with Hive integration for analytics. Processed streaming data with Kafka and AWS Kinesis for real-time analytics. Created data pipelines with AWS Glue and Kinesis Data Firehose for behavioral data ingestion into S3. Analyzed data clusters using AWS EMR tools, leveraging Spark, Hive, and custom MapReduce jobs.Integrated Kafka, Spark, and Hive on AWS EMR to build data pipelines for large datasets. Implemented UNIX and YAML scripting within AWS for workflow automation and deployment with AWS CloudFormation. Migrated large datasets from Oracle RDBMS to AWS using AWS Database Migration Service. Employed PySpark and Spark SQL in AWS EMR for advanced data processing, reducing processing times significantly.Configured AWS Kinesis for optimized batch processing of streaming data. Coordinated and synchronized server cluster operations using AWS services, replacing Zookeeper. Managed and scheduled jobs with AWS Step Functions and Lambda, enhancing efficiency and scalability over Oozie. Maintained code repositories using Git and AWS Code Commit for improved version control and team collaboration.

SQL Server Analyst / Developer / DBA with expertise in SQL Server 2012, 2015, and 2016. Proficient in creating jobs, SQL Mail Agent, Alerts, and scheduling DTS/SSIS Packages. Managed and updated Erwin models (Logical/Physical Data Modeling) for CDS, ADM, and Reference DB. Exported Data Models to PDF and published on SharePoint. Skilled in writing Triggers, Stored Procedures, Functions, and Transact-SQL (TSQL) coding. Maintained source code using Git and GitHub.Developed ETL frameworks with Sqoop, Pig, and Hive for data ingestion. Expert in complex stored procedures, efficient triggers, and creating indexes for performance tuning. Designed ETL data flows and managed data migration using SSIS. Proficient in Dimensional Data Modeling, including SCD, fact, and dimension tables.Experienced in SQL Server performance monitoring, error/event handling, and building Business Intelligence solutions with SSAS Cubes, MDX scripting, and SSRS reports. Extracted data from MySQL into HDFS using Sqoop and implemented automation for deployments with YAML scripts. Leveraged Apache Hive, Pig, HBase, Spark, Zookeeper, Flume, Kafka, and Sqoop for data processing and automation. Implemented data classification algorithms using MapReduce and optimized MapReduce jobs with combiners and distributed cache.

• Experience in developing complex store procedures, efficient triggers, required functions, creating indexes and indexed views for performance.• Excellent Experience in monitoring SQL Server Performance tuning in SQL Server• Involved in designing ETL data flows using SSIS, creating mappings/workflows to extract data from SQL Server and Data Migration and Transformation from Access/Excel Sheets using SQL Server SSIS.• Efficient in Dimensional Data Modeling for Data Mart design, identifying Facts and Dimensions, and developing, fact tables, dimension tables, using Slowly Changing Dimensions (SCD).• Experience in Error and Event Handling: Precedence Constraints, Break Points, Check Points, Logging.• Experienced in Building Cubes and Dimensions with different Architectures and Data Sources for Business Intelligence and writing MDX Scripting.• Working on Developing SSAS Cubes, Aggregation, KPIs, Measures, Partitioning Cube, Data Mining Models and Deploying and Processing SSAS objects.• Experience in creating Ad hoc reports and reports with complex formulas and querying the database for Business Intelligence.• Expertise in developing Parameterized, Chart, Graph, Linked, Dashboard, Scorecards, Report on SSAS Cube using Drill-down, Drill-through and Cascading reports using SSRS.