• Deploy power SCADA system in AWS• Improve and expand analytical ETL pipelines supporting new deals

• Scrape major US ISO & RTO reports and build ETL jobs by using Lambda & Glue.• Monitor AWS services, create alerts & notifications integrated with Email and Slack by using CloudWatch, SNS, SQS.

• QA & QC surface microseismic data from unconventional plays in North America. Perform imputation and data wrangling using Numpy and Pandas. Evaluate processed results and compute visualization graphics by using Matplotlib.• Lead Real-time microseismic processing by using shell scripting and python in Linux. Tested & used Web-based Real-time QC tool. Provided feedback to the development team.• Involve in the development & test of microseismic technology packages and workflow optimization. Design and create data reports using reporting tools to help business executives in their decision-making.

• Set up InsurTech Data Platform from 0 to 1 on AliCloud (Hadoop) with a local team of 5 in Agile environment, conducted daily and weekly meetings with multi-functional teams from Hongkong, India, Eastern Europe, Switzerland, and North America.• Involved in designing & testing strategy with software QA team. Utilized UT, SIT, and UAT data to test data models and ETL jobs in the Data Platform using SQL & Python. Built Power BI reports and presented to executives in the Asia-Pacific branch.• Delivered vendor performance tracking tool by programming and analyzing Azure DevOps data to ensure the project go-live.• Led integration efforts for different insurance products, and different product platforms within the Asia-Pacific region.

• Developed a brand-new downhole microseismic technology platform with a team of 7. The new downhole service accounts for 30% of corporate revenue.• Tested and optimized microseismic software packages and data flows by using Python and shell scripting in Linux.

• Processed seismic data associated with Oil & Gas exploration and production. Produced databases, tools, and reports for analyzing, and root-causing board failure data. Versed in finding patterns and trends in complex, multivariable datasets• Tested and analyzed auto-interpretation program by using machine-learning models to remove data outliers; reduced ~ 20% turnaround time and improved product quality