I'm honored to serve as a Staff Engineer, guiding a talented team of AI/ML and data engineering professionals. My focus lies in architecting, designing, and developing data factory solutions, as well as crafting and deploying AI/ML solutions within the technology and fintech sectors. Drawing upon my expertise in Python, H2O, DataRobot, and both AWS and Azure clouds, I lead our efforts in creating innovative solutions tailored to our clients' requirements.

• Working for data analytics client specialize and global provider in scientific, engineering, technical references as Lead Data Engineer to lead and manage data practices of their product.

• Accountable for end to end data engineering including planning, executing, monitoring and managing all delivery streams into the design and implementation, production sign-off and deployment through to Go-Live phase.

• Lead and architect big data product component on cloud and serverless technologies that include creating data pipelines, data normalization, aggregation from various large data sources and creating search and discovery experience with powerful search engine platforms.

• Instrumental in creating POC for new big data product feature ideation with product team to concluded it as minimum viable product

• Responsible for product roadmap design, project plan, capacity planning, managing milestones, status reporting to management

Project: Risk Mitigation Research Application on Chemical domain

• Data aggregation from wide range of sources, integration, aggregation and normalization

• Created automated serverless data pipelines for import, normalize, aggregation with AWS services

• Created master data source with complex SME specification

• Discoverability of data through Elasticsearch, nested documents and nested querying

Technologies: Python, PySpark, AWS Glue, Java, AWS cloud computing serverless, Data modeling, Elasticsearch, PostgesSQL, Spacy NLP, Terraform

• Working for data analytics client specialize and global provider in scientific, engineering, technical references as Lead Data Engineer to lead and manage data practices of their product.

• Accountable for end to end data engineering including planning, executing, monitoring and managing all delivery streams into the design and implementation, production sign-off and deployment through to Go-Live phase.

• Lead and architect big data product component on cloud and serverless technologies that include creating data pipelines, data normalization, aggregation from various large data sources and creating search and discovery experience with powerful search engine platforms.

• Instrumental in creating POC for new big data product feature ideation with product team to concluded it as minimum viable product.

Project: Risk Mitigation Research Platform on Chemical domain

• Data aggregation from wide range of sources, integration, aggregation and normalization

• Created automated serverless data pipelines for import, normalize, aggregation with AWS services

• Created master data source with complex SME specification

• Discoverability of data through Elasticsearch, nested documents and nested querying

Technologies: Python, PySpark, AWS Glue, Java, AWS cloud computing serverless, Data modeling, Elasticsearch, PostgesSQL, Spacy NLP, Terraform

• Worked on creating a full stack smart search engine and browse platform for research engineers across engineering domains.

• Design, create and manage extensive data pipelines, dealing with large data volumes with AWS Cloud.

• Collaborating with client product team, engineering team to develop POC, design and develop solution, go live, migration projects.

• Published Patent for solutions around information extraction and complex search to make search experience intuitive

Project: Material search engine

• Data analysis, normalization, aggregation batch jobs for large scale data

• Visualizing multi-dimensional data through rich search and discovery graphical experience with Elasticsearch

• Graphical profile and compare experience of material

Project: Engineering reference content search and browse platform

• Data aggregation from wide range of sources, integration, aggregation and normalization

• Created automated batch processing of data for extracting content from pdf, extracting content of pdf with each character coordinates

• Created highly secure, scalable PDF content viewer and metrics data viewer with all customization and personalization options

• Discoverability of data integration through Apache Solr and Elasticsearch

Technologies: Java, Batch Processing, AWS cloud computing, Data modeling, Elasticsearch, MySQL, PHP frameworks, JavaScript frameworks, jQuery

• Worked on creating a full stack smart search engine and browse platform for research engineers across engineering domains.

• Design, create and manage extensive data pipelines, dealing with large data volumes with AWS Cloud.

• Collaborating with client product team, engineering team to develop POC, design and develop solution, go live, migration projects.

• Published Patent for solutions around information extraction and complex search to make search experience intuitive

Project: Material search engine

• Data analysis, normalization, aggregation batch jobs for large scale data

• Visualizing multi-dimensional data through rich search and discovery graphical experience with Elasticsearch

• Graphical profile and compare experience of material

Project: Engineering reference content search and browse platform

• Data aggregation from wide range of sources, integration, aggregation and normalization

• Created automated batch processing of data for extracting content from pdf, extracting content of pdf with each character coordinates

• Created highly secure, scalable PDF content viewer and metrics data viewer with all customization and personalization options

• Discoverability of data integration through Apache Solr and Elasticsearch

Technologies: Java, Batch Processing, AWS cloud computing, Data modeling, Elasticsearch, MySQL, PHP frameworks, JavaScript frameworks, jQuery