Syed Ashar Abbas

Designed a data warehouse using the OLAP schema model in Snowflake for Qualtrics survey data to analyze survey responses. I created and developed data pipelines, including data extraction from a PostgreSQL source database, transformation, movement, and cleansing using Python, SQL, and Fivetran, and loaded data into Snowflake's target table. I also developed complex stored procedures and views to generate business analytics reports on survey data from the OLAP data model. I implemented SCD types to track and manage current and historical OLTP changes in the data warehouse. By optimizing SQL queries and Python scripts, I fixed and fine-tuned post-performance issues of reporting. Finally, I coached and trained other junior staff to increase their SQL, Python, data modeling, and reporting skills.

Environment: SQL Server 2008, SSIS, SSRS, SSAS, Windows 2008 Server

Business: Health and disease analytics

A data warehousing health analytics project using SSIS for ETL, Microsoft SQL Server as the source, and XML as the destination. It parses, transforms, and loads XML-based data from a file and loads it into the destination. I used SSIS control for transformation, lookup, merge, filter, and parsing features. A Create Agent job runs ETL automatically and refreshes the data warehouse. I created an SSAS cube and SSRS reports.

Responsibilities:
• Designed the data warehouse logical and physical schema in Microsoft SQL Server.
• Wrote stored procedures using T-SQL for ETL to load data.
• Used CLR to compress XML after transformation and before load to reduce network traffic.
• Troubleshot performance issues identified in long-running queries.
• Created OLAP using SSAS Star schema, dimensions, facts, and tables in Microsoft SQL Server.
• Created SSRS reports and dashboard.

Environment: Pentaho Data Integration 7, Oracle 11g, PostgreSQL 10.2, Linux 6, Jasper Report

Business: Customer Value Management (CVM) platform

A data warehousing project using Pentaho for ETL, Oracle as a source, and PostgreSQL as a destination. I denormalized the database and loaded text-based CSV data from the source to the destination. I used transformations with lookup files, filters, and missing zip info. Also, I used AWS integration to load data from another legacy environment. I created data masking and cleansing before loading it into the target and a PDI job to run ETL automatically and refresh the data warehouse. I also created the data mart and BI reporting, pulling data from the data warehouse.

Responsibilities:
• Designed the data warehouse logical and physical schema in PostgreSQL.
• Designed the staging area in Oracle to hold TEMP data.
• Wrote scripts for ETL in PL/SQL, PSQL, and control files to load data.
• Troubleshot performance issues during data loading.
• Scheduled an automatic cron job in Linux for ETL.
• Designed OLAP Star Schema, dimensions, and fact tables in Oracle.
• Created reports and a dashboard using Pentaho reports.
• Created auditing schema to keep track of ETL job status.

I successfully optimized a MongoDB database performance for multiple clients, significantly improving query response times and overall system performance. I provided expert guidance and training to client teams on best practices for MongoDB performance optimization, including query optimization, index creation, and schema design.