Data Analysis Developer in Patna, Bihar, India

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 flow of data and develop 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) The submits it by making a call to 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 near real-time view to store the conversion in terms of sales, traffic, conversion, and associate contributions which are 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 S3 to a Redshift database.

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

Work Done:
• Create 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.
• Setup AWS RDS PostgreSQL for keeping costs within parameters and getting 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 to understand and make the right decision at the right time to increase sales with customers online and in 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, S3, Google Analytics API, and IBM Silverpop.
• Created Python scripts to automate the data loading.

MOD is the biggest employer in the Netherlands with nearly 90,000 employees all over the world.

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 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 database amounting 300 GB out of 2 TB.
• Worked on data modeling, physical design, and database administration.
• Performance-tuned the largest tables with up to 150 partitions, amounting to 400 GB 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 the reintegration of 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 ranging from the logical and physical design, administration, PL/SQL, 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.