A K Email & Phone Number

San Diego, California, United States

# Proficient in a wide spectrum of AWS services, including EC2, S3, RDS, Lambda, VPC, IAM, DMS, and more.# Worked with AWS cloud services like Glue, EMR, S3, Redshift, and Athena for big data development.# Used various components of the Hadoop ecosystem on AWS EMR.# Extensively worked on Spark using Python for Computational Analytics.# Implemented and optimized ETL processes using Databricks.# Implemented ETL processes to streamline data import into AWS Redshift.# Developed custom ETL scripts in Python within AWS Glue.# Designed end-to-end ETL strategies for loading data from OLTP systems to OLAP systems.# Proficient in Python for data manipulation and ETL tasks.# Utilized SQL for data cleaning, aggregation, and reporting.# Developed Python Flask APIs for data extraction and transformation.# Integrated AWS Glue with various AWS services to create data pipelines.# Utilized SQL to clean and aggregate data from various sources.# Performed data visualization and designed dashboards with Tableau.# Created complex reports, including charts and graphs, for data interpretation.# Installed/Configured/Maintained Apache Hadoop clusters.# Set up monitoring and logging solutions on AWS using CloudWatch and CloudTrail.# Worked with various data source types, including SQL server, Teradata, flat-files, JSON, and more.# Actively involved in data project life cycles, including data extraction, cleaning, and visualization.# Utilized Python libraries, including Pandas, SciKit-Learn, Matplotlib, NLTK, Flask, and more.# Cleaned and processed data using Python packages and automated data cleaning.# Utilized GitHub for version control and coordinated team development.# Collaborated with technical and non-technical stakeholders to understand data needs.# Leveraged AWS Glue Data Catalog for centralized metadata repositories, enhancing data discoverability and lineage tracking.

Charlotte, North Carolina, United States

# Involved in converting Hive/SQL queries into Spark transformations using Spark Data frames and Python.# Developed real-time data processing applications using Scala, Python, and Apache Spark Streaming.# Designed and implemented end-to-end data orchestration solutions in Azure Data Factory.# Integrated on-premises data sources with Azure cloud services using Azure Data Factory's self-hosted integration runtime.# Developed ETL pipelines using Python, Scala, Spark, and Spark Streaming.# Generated graphical reports using Python packages NumPy and Matplotlib.# Created robust and scalable Python Flask APIs to facilitate data extraction from various sources.# Implemented Python Flask endpoints to establish secure connections with diverse data sources.# Developed Python scripts to parse JSON and CSV documents for data loading.# Responsible for cluster maintenance, adding and removing cluster nodes, and cluster monitoring.# Extensively involved in the installation and configuration of Cloudera Hadoop.# Integrated Delta Lake with Azure Databricks for data reliability and ACID transactions.# Implemented DevOps practices using Azure DevOps services for automation and continuous integration (CI) and continuous deployment (CD) pipelines.# Experience in Database Design and development with Business Intelligence using SQL Server, SSIS, SSAS, and data warehousing (Star Schema, Snowflake Schema).# Utilized Hive, Pig, and UDFs to capture and analyze data.# Loaded data from relational database management systems using Sqoop.# Integrated and migrated data from external sources, such as Snowpipe, Snowflake Data Loader, and other ETL tools.# Used Oozie Scheduler systems to automate pipeline workflows and orchestrate Hive/MapReduce jobs.# Worked with various cloud services, including Azure Event Hubs, Kafka, and Flume.# Maintained a proactive approach to staying current with the latest advancements in Python and emerging libraries and frameworks.

Fort Worth, Texas, United States

# Experience in designing, architecting, and deploying scalable and highly available applications on AWS.# Designed and implemented data processing pipelines using AWS Kinesis.# Redesigned cloud-based data warehouse for enhanced security and performance.# Developed and maintained data pipelines to ingest streaming and transactional data using Spark, Kafka, Java, and Python.# Implemented ETL processes to streamline data import into Redshift data warehouse.# Developed MapReduce jobs and Spark applications for data processing and analysis.# Utilized Python modules like Pandas, NumPy, Matplotlib, and SciPy for data analysis.# Developed custom Python scripts for automation and optimization.# Developed APIs and data models conforming to FHIR specifications for accurate and secure data exchange.# Designed and implemented complex data models using Tableau.# Created Tableau reports with complex calculations and ad-hoc reporting.# Developed Snowflake Procedures for executing branching and looping.# Possess deep understanding of security best practices, including role-based access control (RBAC) and compliance standards such as GDPR and HIPAA.# Analyzed data lineage processes to identify data quality issues and data governance gaps.# Used Snowflake cloud data warehouse and developed complex Snowflake scripts.# Developed AWS Redshift queries for meaningful insights.# Integrated data from various sources for downstream analysis.# Designed and implemented data pipelines using AWS services such as Kinesis, EMR, and Glue.# Worked with Spark ecosystem using Python, Scala, and HIVE queries on different data formats.# Used Spark SQL API in Spark for data extraction, loading, and SQL queries.# Participated in code reviews and contributed to best practices for data engineering on AWS.