Kamal Bisht

Designed and implemented an optimized MIS (Management Information System) pipeline, reducing the Month-End Close (MEC) process from 5 days to just 1 day. Automated workflows for multiple finance businesses, including CSPL, CFSPL, and NBFC, enhancing operational efficiency. Developed an ANPR (Automatic Number Plate Recognition) software for the company warehouse, significantly reducing costs through automation. Built a GenAI-powered MIS dashboarding solution, enabling end users to dynamically generate custom dashboards with AI-driven business insights and predictive trend analysis, enhancing strategic decision-making.

Worked on a project related to a Finance Model for Reckitt. Successfully managed and migrated 2 major and 1 minor project. Collaborated with the team to design pipeline architecture and data modelling. Developed and maintained comprehensive documentation for pipeline-related processes. Worked closely with clients and cross-functional teams, including Business Analysts (BA), Data Analysts (DA), and Data Scientists (DS). Managed, monitored, and upheld the operational integrity of data pipelines. Conducted rigorous data validation at Fusion and Power BI (PBI) layers to ensure data accuracy. Enhanced pipelines by incorporating new features such as key performance indicators (KPIs), Facts and Dimension Data. Implemented pipeline optimizations to enhance performance and efficiency. Proficiently utilized technologies including Azure Data Factory (ADF), Blob Storage, Azure Data Lake Storage (ADLS), databases (MSSQL), Databricks, PySpark, Data Warehousing (DWH), Azure Analysis Services (AAS), Power BI (PBI), Excel, and Fusion (SAP HANA).

Worked on a project related to a future Prediction model for Reckitt. Predicted current market prices and future prices of commodities, considering factors such as demand and supply. Extracted data from 3rd Party in CSV/JSON file format, API from the front-end team, and created a pipeline to extract the data from the WebSite. Stored the data in Azure Data Lake Storage (ADLS). Utilized Azure Data Factory (ADF) for data ingestion and DataBricks for the data Transformation into the desired format. Stored the transformed data into ADLS location for use by the Data Analytics and Data Science teams for prediction purposes. Technologies used: Azure Data Factory, Azure Data Lake Storage, Data Bricks, Blob Storage, Pyspark.

Involved in a Data Migration Project for Goldman Sachs. Focused on data ingestion, processing, transformation, and cleaning using Python. Wrote codes in Python and Scala to meet business requirements. Collaborated with clients to gather information and design logic. Refactored and migrated code, validated pipeline by running data on old and new pipeline. Conducted extensive investigation of critical data issues in the late stages. Experienced in working with Hadoop and its tech stack (MapReduce, HBase, Hive), Apache Spark, Python, Java, Scala, AWS, GitHub & GitLab, and CI/CD Pipeline.

Collaborated with UiPath clients to design and implement an autonomous software pipeline for email notifications, leveraging the UiPath Framework to enhance automation efficiency and reliability.