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Data Engineer requirements:
Design, Deploy, and maintain solutions on MS Azure using different Cloud & Big Data Technologies.
Manage the full life cycle of a Data Lake / Big Data solutions from requirement gathering and analysis to platform selection, design of the architecture, and deployment.
Be responsible for implementing solutions that can scale on Cloud.
Collaborate with a team of business domain experts, data scientists, and application developers to develop BD solutions.
Explore and learn new technologies for creative business problem solving and mentor a team of Data Engineers.
Power BI requirements:
Understand business requirements in BI context and design data models to transform raw data into meaningful insights.
Convert business requirements into technical specifications and decide timeline to accomplish.
Create dashboards and interactive visual reports using Power BI
Identify key performance indicators (KPIs) with clear objectives and consistently monitor them.
Analyzing data and present data through reports that aid decision-making
Create relationships between data and develop tabular and other multidimensional data models.
Chart creation and data documentation explaining algorithms, parameters, models, and relations.
Design, develop, test, and deploy Power BI scripts and perform detailed analytics.
Perform DAX queries and functions in Power BI
Analyze current ETL process, define and design new systems.
Redefine and make technical/strategic changes to enhance existing Business Intelligence systems.
Create customize charts and custom calculations as per requirements.
SQL querying for best results, use of filters and graphs for better understanding of data
Work with users and team members at all levels for performance improvement and suggestions
Job Requirements
Strong Hands-on experience in implementing Data Lake with technologies like - Azure Data Factory (ADF), Azure Synapse, ADLS, Databricks, Azure Synapse Analytics, SQL Database, Power BI, Azure ML, Azure Databricks
Strong programming & debugging skills either in SQL, Python and Scala/Java. Experience in building REST services is good to have.
Experience in supporting Data Science teams in consuming the data in a secure and governed manner.
Experience deploying Machine Learning solutions into production commercial environments.
Good, understand, and Experience of using CI/CD with Git, Jenkins / Azure DevOps. Understanding of branching strategies, pull requests, and code reviews.
Experience in setting up cloud-computing infrastructure solutions (in Dev Environment).
Experience in using NoSQL Databases and Data Modelling in Hive
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