NEERAJ SHARMA

Results-driven Staff Software Engineer at IBM ISL with over 12 years of experience in developing and implementing innovative software solutions. Adept at leading cross-functional teams and guiding projects through the full software development lifecycle. Skilled in various programming languages and technologies, including Java, J2EE, Python, and cloud computing platforms. Proven track record of delivering high-quality, scalable software products that meet and exceed customer expectations. Strong communication and collaboration skills, with a demonstrated ability to work effectively in fast-paced, dynamic environments. Passionate about leveraging technology to drive business success and improve overall efficiency.

CrushIt Team Delivery Excellence Award

IBM December 2017

I have worked with my team on development of Kafka notification for IBM MDM (v 11.6.0.3).

The adoption of open-source where applicable into the analytics portfolio is one of the Focus 5 areas.

My team has developed the platform to add the open-source Kafka messaging and streaming to the MDM product portfolio.

By integrating Kafka into MDM, team delivered in the following cool capabilities:

First, based on the Kafka events aggregation services have been developed to feed a functional dashboard allowing Data Stewards to see what is happening in the MDM core engine such duplicate task events, service workload distribution etc.

Second: Critical event and notification infrastructure to notify other systems about critical master have been enabled to leverage Kafka.

Third: Sending data quality tasks to the BPM platform for processdriven data stewardship has been Kafka-enabled. As a result - our MDM customers can now use the open-source Kafka platform for all messaging and data streaming functionality needed by MDM solutions.

On the Spot Award

Dell April 2014

I have worked on integrating the Oracle Agile PLM with Oracle Enterprise data for Product quality (EDQP) tool. We get the data from the Business team which is unorganized and unstructured data. Later EDQP tool for ETL (extract, transform and load) processes the data based on the defined business rules. Now this structured data will be imported into the Agile PLM database through the EDQP load process. I have been awarded On the Spot award for excellent performance and quality delivered for my contribution in Dell Master Data Management project-I & II.

March 2016 Machine Learning Stanford | Coursera https://www.coursera.org/account/accomplishments/verify/N8VPYK6BVSSM

Supervised Machine Learning: Regression and Classification Built machine learning models in Python using popular machine learning libraries NumPy & scikit-learn. Built & trained supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression.