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Sophie kalsoom User

@sophiekalsoomuser

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Senior Data Engineer at UHY who builds production data platforms and ETL/ELT pipelines across AWS, Azure, and GCP.

United States

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At UHY, I design AWS-based data platforms and production ETL/ELT pipelines for enterprise analytics and reporting. I use Amazon Redshift, Amazon S3, and AWS Glue to integrate databases, APIs, and cloud storage, with monitoring and validation to support reliable data delivery.

At Amgen, I built pipelines for clinical and operational datasets with Azure Databricks, PySpark, and Delta Lake. I implemented Medallion Architecture and streaming workflows, and supported data quality, Spark optimization, and production reliability.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

UHY

Jan 2020 - Present (6 years 10 months)

Designed scalable AWS-based data platforms and engineered production ETL/ELT pipelines. Automated workflows with AWS Glue and Step Functions, implementing monitoring, retry handling, and data-quality controls.

AWS Amazon Redshift S3 AWS Glue Python SQL ETL ELT Step Functions IAM KMS Data Modeling Data Quality Monitoring Cloudwatch

Data Engineer

Amgen

Jan 2017 - Dec 2019 (2 years 11 months)

Built enterprise data pipelines using Azure Databricks, Apache Spark, PySpark, and Delta Lake. Implemented Medallion Architecture and integrated streaming sources with Azure Event Hubs and Spark Structured Streaming.

Azure Databricks Apache Spark PySpark Delta Lake Azure Data Factory Spark SQL Python Medallion Architecture Azure Event Hubs Spark Structured Streaming Data Quality Data Validation Monitoring

Junior Data Engineer

Blue Orange Digital

Oct 2014 - Dec 2016 (2 years 2 months)

Supported data engineering workflows and SQL-based data preparation activities. Assisted with pipeline development, data validation, and production data operations.

SQL Data Validation Data Preparation Troubleshooting Documentation

ETL Developer

Centric Consulting

Jan 2014 - Sep 2014 (8 months)

Developed enterprise ETL pipelines using SSIS, Informatica PowerCenter, SQL Server, T-SQL, and PL/SQL. Built dimensional data models including Type 2 Slowly Changing Dimensions and optimized queries.