- Work with stakeholders including the Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.- Create data tools for analytics and data scientist team members to strive for greater functionality in our data systems.- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability.- Created ingestion and integration pipelines that bring data from the source pass it through the different layers of the Data Lake and then make it available to the business users in the Enterprise Data Warehouse built using the concepts of Data Vault.

• Generated an automated report for anomaly detection in machine parts using the TensorFlow library in Python that led to a 60% reduction in the false positives.• Reduced resampling time of huge flight datasets by 75% by switching the implementation of the resampling algorithm from Numpy to Pandas.• Used test driven development methodology to set up CI/CD pipeline on Azure DevOps which facilitated automated testing to reduce the workload on QA team.• Predicted the steps involved in a new change request using a NLP model trained on data from past 20 years to reduce the time taken for this change.• Improved team efficiency by suggesting ways to collect data on team performance, analyzing and visualizing it using PowerBI and finally recommending ideas to minimize bottlenecks.

• Identified persistent processes and generated related graphs and charts using stored procedures and functions in SSRS to decrease the total performance time.• Developed an automated periodic backup system using T-SQL in SSMS, that selectively backs up configuration tables depending on the corresponding changes in the database schema.• Improved team productivity by setting up a system that instantly send email about the error causing process.