Data Engineer, Data Analyst, Data Scientist and AI Engineer
**** **** ****
5 Jahre Erfahrung
Not Available
Mid-Level
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Data Engineer with 5 years of experience executing data-driven solutions to increase efficiency, accuracy, and utility of data processing. Experienced in creating data pipelines, predictive and descriptive analytics, data visualization to deliver insights and implement solutions to complex business problems.
Created and updated the Azure Data Factory (ADF), Synapse Analytics pipelines and Power BI reports by implementing a new business logic.Delivered the changes within the scrum period.Decreased SLA breach of all BI platform ServiceNow tickets by 50% by prioritizing, taking appropriate actions and providing resolutions as soon as possible.Documented all the Data Engineering and Visualization process with technical details on Confluence and regularly updated with new versions.Monitored the ADF Triggers and other Azure scheduled jobs on daily basis, so that latest data is available for the reporting.Fixed the errors and job failures when occurred.Skills: Data Build Tool (DBT) · Snowflake · SQL · Team Building · Python · Microsoft SQL Server · SSIS · PySpark · Data Analytics · Data Modeling · Azure Data Lake · Data Warehousing · Database Design · Data Integration · Azure Databricks · Azure Data Factory · Microsoft Power BI · SQL Server Analysis Services (SSAS) · Confluence · ServiceNow · Problem Solving · Leadership · Strategic Thinking · Interpersonal Skills
M
DATA ENGINEER
MERCEDES BENZ RESEARCH AND DEVELOPMENT
Aug'2018 - Jul'2021
Took ownership of database tasks for the BI Reporting team. Which includes creating data pipelines using SQL Server, SSIS, ADF and SSMS, implementing business logics, creating data models in the Azure Analysis Services and creating Power BI reports.Increased performance by 50% & maintained 99.9% uptime of the reporting platform DATA BEE.Created a solution recommendation for a ticketing system using Python and Machine learning.Created different Database objects like Stored Procedures (SP), Functions, Triggers, Indexes, and Views using T-SQL Script.
Timesheets Utilization Project uses different systems like Aura, Workfront Global, D365 as a data source. These data is collected processed and business logic is applied on top of it. After that Semantic layer is created and reports are built on top of it.
I have contributed to investigate the issues and fixing the issues in the report, data and the data pipelines. I have also contributed while adding new features to the report and the backend logic.
Technologies used: MS SQL server, SSMS, SSIS, Tableau, Power BI, Azure technologies
The platform which provides reporting service, data view access to other projects inside Dentsu. Uses
D365, Salesforce as major data source. Took responsibility as a support engineer for all the BI projects.
Delivered change request on time by making changes to the dashboards and ADF pipelines.
Tech stack – Power BI, Azure SQL, Azure Data Factory, ELT.
D365 F&O is a Finance Reporting Project which uses D365 , Concur as a data source and the data is gathered and processed on op of that Tableau and Power BI Reports are built.
I have contributed to managing the access, Solving the report issues, Monitoring the Data Loads and majorly for the support tasks.
Technologies Used: SSIS, SSMS, MS SQL Server, Tableau, Power BI, Azure technologies
The reporting platform used by various teams inside Daimler. Took responsibility for handling
complete backend which includes extracting data from various sources, loading, and transforming it
based on the requirement. All the steps are automated. Analysed the data and created the report.
Tech stack- MS SQL Server, ETL,SSIS Data Pipeline, Data Preparation, Data Cleaning, and Data
Analysis.
Identified potential loan customers for Thera Bank using classification techniques. Compared models
built with Logistic Regression and KNN algorithm to select the best performing one.
Tech stack – Python Libraries, Logistic Regression, KNN, and Classification.
This project involved building recommendation systems for Amazon products. A popularity-based
model and a collaborative Filtering model were used and evaluated to recommend top-10 products
for a user.
Tech stack - Collaborative Filtering, Recommender Systems, and Python Libraries.