Syed Akbar Naqvi

RMS Connector works as an interface between a retail merchandising system (RMS) and Oracle retail data warehouse (ORDM).

It easily integrates the RMS data feeds into ORDM for all levels of sales and inventory reporting and is used by the top retailers in the US and Central America.

Work Done:
• Built all the ETL and data flow from Flat files to the Oracle database using the Oracle Warehouse Builder.
• Developed PLSQL packages and procedures for all new files from RMS to ORDM.
• Made recommendations and was involved in the planning and setup of the database architecture for production.
• Tuned the database and report performance.
• Set up ETL automation using Oracle Scheduler Chains.

This project is a SaaS application running on AWS.

The architecture includes:
• Redshift: for the storage and reporting of data
• Python: for data processing based on user input
• SOS Belin Scheduler: a scheduler for executing Python scripts based on user inputs asynchronously
• UI: for user input
• Tableau: for reporting on segments created
• Environment: EC2, Redshift

My job was to design and develop the end-to-end data flow and the required APIs for data processing.

Flow:
1) The user creates a segment model by selecting different KPIs related to the customer and then submits the job.
2) Then submits it by calling SOS REST and the Python library for customer segmentation.
3) Examines the input provided in the REST call and, based on that, makes the next decision.
4) The data is then processed and is ready to be picked up by Tableau.

An initial data load is required for customer transactions and KPI preparation.

This is a mobile-based application for the near real-time view to store the conversion in terms of sales, traffic, conversion, and associate contributions crucial for any store manager.

Work done:
• Developed the data model for layering, including the dimensions, facts, and aggregates.
• Built ETL procedures using Talend, PLINK, Python, SOS, and Berlin Scheduler.
• Wrote shell scripts to manage the data feeds, and they used Python scripts to process the files from Amazon S3 to a Redshift database.

Labor Scheduler helps the store manager connect predicted traffic demand with staff productivity and availability to optimize conversion.

Work done:
• Created the end-to-end data model.
• Developed the procedures and APIs for the data operation from the REST APIs.
• Implemented version control in the DB rows.
• Set up AWS RDS PostgreSQL to keep costs within parameters and get high throughput.
• Set up data interactions between multiple databases using PostgreSQL database links to a Redshift database for extracting analytical data.
• Rigorously used PL/pgSQL, Python, PostgreSQL, and Redshift for managing data.

The IX Marketing module helps retailers understand and make the right decision at the right time to increase sales with customers online and in the store.

Work done:
• Created the data model and set up the environment using the Redshift database.
• Extracted and loaded data from multiple sources like SFTP, Amazon S3, Google Analytics API, and IBM Silverpop.
• Created Python scripts to automate data loading.

MOD is the biggest employer in the Netherlands, with nearly 90,000 employees worldwide.

Work done:
• Implemented new design recommended during POC.
• Installed Oracle streams between Oracle 10g and Oracle 9i databases for real-time extractions.
• Developed the new logic for ETL processes for near real-time transformation in ODM.
• Set up batch jobs for the periodical load of transformed data into CDM from ODM for Cognos reporting.
• Configured selected PeopleSoft HRMS tables for the Streams configuration.
• Performance-tuned their current system.

Eneco Energies is one of the largest energy companies in the Netherlands.

Work done:
• Successfully designed and implemented an MVS system into the SOA-enabled architecture.
• Tuned the physical model for performance improvement in ETL processes.
• Successfully segregated all the objects pertaining to one functional area into separate databases amounting to 300GB out of 2TB.
• Worked on data modeling, physical design, and database administration.
• Performance-tuned the largest tables with up to 150 partitions, amounting to 400GB alone.
• Developed a mechanism to automate the setup of a testing environment.

The policy administration system interfaces primarily with the tax and administration department and is integral to reintegrating work processes and employee tax/income information processing.

Role: Database Team Member | DBA
Work Done:
• Worked on all activities of an Oracle DBA and developer, from logical and physical design, administration, PL/SQL, and scripting to communication with the front office about the CRs and use cases.
• Created and modified the DSS and OLTP physical data model.
• Performed database administration—sizing, backup recovery strategy planning, and implementation.
• Database design and administration.
• Database maintenance and release activities.
• Performance-tuned.

FieldID is a centralized database for processing millions of boundaries and assigning a Unique ID for the given boundary and its associate Field. FieldID's primary goal is to provide field-level agronomy data that different farmers and food producers can use in making informed farming decisions and controlling carbon compliance.

I designed the data model, architecture flow, and building data pipelines. Tools and technologies used, Python, SQL, PGSQL, PostgreSQL, Redshift, Confluent Kafka, Apache Airflow, PostGIS, etc.

Field Stories provides end users access to large and processed agronomy datasets which could be purchased by the farmers or food producers. The tools provide a GUI with maps and boundaries with different biological data about the soil of selected fields.

Using this data, the farmers can make informed decisions about using chemicals and fertilizers to achieve optimal cultivation and increased harvest. I analyzed the data and built models and pipelines for data from different sources.

I was also responsible for transforming the data and mapping it with different Global Boundary IDs to provide easy access from the GUI.

Shop Circle is a startup with a main focus on the enhancement of Shopify marketplace applications. They buy and enhance already running and perfuming applications by upscaling their capabilities by capturing and mining event data from Shopify.

I worked on an internal but very important dashboard that business owners use to identify app performance and then suggest business improvements to the client.

I built the data model for KPIs to be used for creating dashboards and charts using Tableau Online. I built the transformations using SQL, DBT, Snowflake, and Python. I developed charts for multiple KPIs like MRR, ARR, Churn, APR, Cohorts, etc., to name a few.

Perfect Store project was a high stake project done for identifying the best-performing stores based on parameters such as how the Pepsico products are placed inside any particular store and the overall sales performance for all the stores.

The POC should identify the best-performing store and then the shelf and product placement, competitor product sales, rows, and location of the shelf, etc. All this data is then used to build and adjust the other stores so that similar kinds of sales can be achieved. I built the data pipelines using Azure Stack, Azure Databricks, Azure Data Lake, and Azure Data Factory.