shristi khanal

# Responsible for working with various teams on a project to develop analytics- based solution to target customer subscribers specifically. # Resposible for persing and transforming XML data using Xpath and XML persers. Excellent ambiguous data cleansing, Talend data Integration, oracle data integrator, transforms, and integrates structured data and delivers data to a scalable data warehouse platform using traditional ETL (Extract, Transform, Load) tools and methodologies to collect of data from various sources into a single data warehouse. # using HVR for data replication , integrating HVR with Azure services such as Azure SQL Database, Azure Synapse Analytics, and Azure Data Lake. Used Erwin to model and transform date requirements in to data models and maintaining the database schema. # Used Cognos for scheduler to automate report generation. # Wrote production level Machine Learning classification models and ensemble classification models from scratch using Python and PySpark to predict binary values for certain attributes in certain time frame. # Expert to use azure synapse anlytics to ingest, transform and analyze large dataset from various sources Like database, flatfiles, APIs and cloud storage (azure blob storage and data lake storage) # Expert in SQL to use multiple join conditions, windows function and use clause function and can quickly analyse data with pyspark and handle large dataset across cluster efficiently. Profile and optimize Scala applications for performance, ensuring they run efficiently under load. # Integrate Scala applications into CI/CD pipelines for automated testing and deployment. # Performed all necessary day-to-day GIT support for different projects, Responsible for design and maintenance of the GIT Repositories, and the access control strategies.
# Involved in design and implementation of reference data management solution using informatica. # Writing UNIX shell scripts to automate the jobs and scheduling cron jobs for job automation using commands with Crontab. # following proper steps to effectively use traditional data modeling( hierarchical, network and relational) to build robust, scalable, and maintainable data systems that meet business needs. Perform ETL, ETL AB-initio tasks in extracting the data from databases using SAS procedures # A highly immersive Data Container involving Data Manipulation & Visualization in Power BI, tableau, Web Scraping, Machine Learning, Python, R, programming, MYSQL, PLSQL, GIT, Unix Commands, TSQL, SQLserver 2019, MongoDB, Hadoop Expert to ingesting several data sets into delta Lake, and do some transformations using Databricks or spark based on the use cases.Designed medallion data architecture and expert data services ecosystem spanning Relational, NoSQL, and Big Data technologies.

Responsibilities:
# Performed data preprocessing and feature engineering for further predictive analytics using Python Pandas. # Files extracted from Hadoop and dropped on daily hourly basis into S3. Working with Data governance and create and develop Data quality rules to design various models and processes. # Responsible for documenting XML data and application and writing documentation how is the data structure and explain how to implement it for integration.
• Involved in all the steps and scope of the project reference data approach to MDM, have created a Data Dictionary and Mapping from Sources to the Target in MDM Data Model. # Used Spark and Pandas to calculate the moving average and RSI score of the stocks and generated them into data warehouse. # Expert to Implement Medallion Architecture and involves a structured approach to organize and process data effectively, data ingestion in bronze layer(raw layer) data processing in silver layer and data aggregation in gold layer.
• Exploring with Spark to improve the performance and optimization of the existing algorithms in Hadoop using Spark context, Spark-SQL, PostgreSQL, Data Frame, OpenShift, Talend, pair RDD's. # Created and maintained SQL Server scheduled jobs, executing stored procedures for the purpose of extracting data from Oracle into SQL Server. Extensively used Tableau for customer marketing data and visualization data in Power BI. # Generated report on predictive analytics using Python and Tableau including visualizing model performance and prediction results. # Selected and generated data into csv files and stored them into AWS S3 by using AWS EC2 and then structured and stored in AWS Redshift.
◦ Utilized Agile and Scrum methodology for team and project management. # Expert to organizations manage and protect sensitive information effectively while maintaining compliance with both HIPAA and GDPR. Used Git for version control with colleagues.

Environment: Spark SQL, Azure, Spark XML, Python, R, SQL, Tableau, Azure, Agile, SAP, Oracle, AWS, Data integrator, Data Container, GitHub, AWS EMR/EC2/S3/Redshift, Oracle 12c, SQL Server, Git.

Identified and addressed the key issues that organization was facing from the customer side in terms of lack of feedback, sudden inactivity and friction while accessing the services. Performed the Talend data profiling using k-means clustering to make the clusters of the population and checked the anomalies in it and did the cleansing of the data by using SPSS. Performed the Comparative analysis of churning and non-churning profiles to generalize the model. Performed the predictive analysis of the extracted data by using machine learning algorithms like Regression analysis, Support vector machines, decision tree and neural networks to predict the churn. # Visualizing the results of the analysis over the Tableau by making the Histograms, pie charts, box plots and bubble charts and other charts. Created the Dashboard and deployed it on the servers. # Creating Pipelines in ADF using Linked Services/Datasets/Pipeline/ to Extract, Transform, and load data from different sources like Azure SQL, Azure cosmos DB, Azure data brick, Blob storage, Azure, SQL, KPI Data warehouse, write-back tool