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Srikanth (RID : qe71l1byln64)

designation   Azure Data Engineer

location   Location : Hyderabad, India,

experience   Experience : 3 Year

rate   Rate: $14 / Hourly

Availability   Availability : Immediate

Work From   Work From : Offsite

designation   Category : Information Technology & Services

Shortlisted : 1
Total Views : 96
Key Skills
Azure Devoops Git SQL Terraform Python

Summary - Having Around 3+ years of working experience as SQL Developer and Azure Data Engineer. Knowledge in components like Terraform, Azure Admin, Azure Ticket tool, Azure active directory, Azure cloud are my core strengths.

Technical Skills:


Azure DBA, SQL DBA,python.

SQL server tool

SSMS, Profiler, Query Analyzer,


MS SQL Server

Operation system



On-Premise SQL, IaaS SQL, PaaS, Azure VMs, Recovery services vault, storage account, ADLS, ADF, Azure SQL DB Managed instance, Geo-replication, Failover groups, Sync to other dbs. DMA, DMS, Migration, Automation accounts, Azure active directory, Azure analysis services, Storage explorer, PowerBI, Azure DevOps, Azure monitoring, Key vaults, Application support, Data bricks. SSL certificate renewal

Ticketing tools

Orbit and Azure Devops, SNOW,DMA, DMS

Operating Systems:


Project Experience:


Plant and Perform tools


It is a migration project. We need to keep data centralized and financial applications are there mostly. Analyzing the method of transforming existing data into a format for the new environment and the loading of this data into other database structures

Review existing migration tools and provide recommendations for improving the performance of the migration process.


  • Configuring Azure Services PaaS, IaaS, and Migrated databases from on-premises to Azure.
  • Configured development and test environment on Azure.
  • Creating and monitoring Azure SQL Server databases.
  • Configured and Maintained Elastic DB pools.
  • Configuring and monitoring Geo-Replication
  • Configured failover groups to provide high availability for PAAS SQL Server.
  • Database Synchronizations using HUB-MEMBER Topologies.
  • Configuring backups and restoring databases from the recovery vault.
  • Configuring Server level and database auditing for SQL server.
  • Configuring CPU, Disk, DTU usage alerts, and Monitoring for Azure SQL servers.
  • Performance Metrics and Plan Management.
  • Configured server level and database level firewalls as per security compliance.
  • Implementing TDE (Transparent Data Encryption) and DDM (Dynamic Data Masking) Features.
  • Used Database Migration Assistant tool to migrate the large databases to PAAS Server and VM server.
  • Deployed SSIS packages to the ADF with the lift and shift method.
  • Configured high availability integration run time with two nodes.
  • Created a linked service to create the dataset with dynamic content.
  • ADF pipelines monitoring and fixing the issues accordingly.
  • Worked on Key-vault and provided access with access policies and saved keys, certificates, and secrets.
  • Creating the ADF pipelines as per the requirement.

Team Size

10 Members.

Technology and Tools

SSMS tools, Azure, Devoops, Terraform, Git, SQL DB

Project Duration

1 year and 10 months


Density based smart traffic control


Traffic congestion is one of the major modern day crises in every big city in the world. To automate the traffic time and allocate the required time for green signal. This application will behave effectively.

Based on the density of traffic we allocate the green signal time. This will reduce the less dense traffic with more time allocation.


  • In this paper author is describing concept to control or automate green traffic signal
  • allotment time based on congestion available at road side using Canny Edge Detection
  • Algorithms. To implement this technique, we are uploading current traffic image to the
  • application and application will extract edges from images and if there is more traffic then
  • there will be more number of edges with white color and if uploaded image contains less
  • traffic then it will have less number of white color edges. Empty edges will have black
  • color with value 0. By counting number of non-zeroes white pixels we will have
  • complete idea of available traffic and based on that we will allocate time to green signal. If
  • less traffic is there then green signal time will be less otherwise green signal allocation
  • time will be more.
  • Keywords: Smart Traffic Control, Density based Traffic Control, EdgeDetection, Image
  • processing in Traffic Control.

Team Size

5 Members.

Technology and Tools

Python, terraform, git, Azure active directory, V networking

Project Duration

1 year and 6 months

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