Vetted Talent
Monica Kherajani
Vetted Talent
Disciplined and insightful data engineer with 5.5 years of experience in building real time data pipelines, ETL pipelines, implementing security aspects, creating visualizations and providing useful business insights.
- Role
Lead Data Engineer
- Years of Experience
6 years
Skillsets
- Kibana
- AWS Lambda
- Unix
- Power BI
- NoSQL
- Linux
- Step functions
- Logstash
- EMR
- Data Analysis
- CI/CD
- AWS
- Airflow - 1 Years
- Logstash
- Glue
- S3 - 3 Years
- Python
- Kafka - 2 Years
- CI/CD - 2 Years
- Jira
- Github
- Shell Scripting - 4 Years
- Redshift - 5 Years
- Data Analysis - 05 Years
- AWS - 3 Years
- SQL - 4 Years
- SQL
- PySpark - 4 Years
- Elasticsearch - 4 Years
- Elasticsearch
- Python - 5 Years
Vetted For
13Skills
- Roles & Skills
- Results
- Details
Data Engineer || (Remote)AI Screening- 72%
- Skills assessed :Airflow, Data Governance, machine learning and data science, BigQuery, ETL processes, Hive, Relational DB, Snowflake, Hadoop, Java, Postgre SQL, Python, SQL
- Score: 65/90
Professional Summary
6Years
- Mar, 2022 - Present4 yr 4 months
Lead Data Engineer
Kyndryl - Sep, 2019 - Feb, 20222 yr 5 months
Data Engineer
Larsen Toubro Infotech
Applications & Tools Known
Python
Elasticsearch
Kibana
AWS
S3
Glue
Redshift
AWS Lambda
Jira
Power BI
Kafka
Cassandra
Logstash
Spark
Kibana
Github
Agile
Shell scripting
CI/CD
SQL
NOSQL
Unix/Linux
Kafka
GitHub
Agile
Scrum
Airflow
CI/CD
Kibana
AWS
SQL
Data Analysis
Agile
Shell Scripting
CI/CD
Work History
6Years
Lead Data Engineer
KyndrylMar, 2022 - Present4 yr 4 months
Developed ETL data processing pipeline using Python, Elasticsearch, Kibana. Read multiple csv files, performed transformations using NumPy and Pandas, delivering business insights. Engineered and deployed ETL pipeline in AWS using Glue, S3, Redshift, and Power BI, extracting data from Jira API and Project Rak API to analyze teams, boards, and sprints, optimizing resource allocation for 30% efficiency boost. Integrated data from APIs to build analytical dashboards. Automated Backup reporting, reducing manual efforts by 40%. Implemented best practices for logging. Communicated client needs, guiding team members. Worked on POC using AWS Data Migration service to migrate data from Oracle to Redshift. Established POC to read data from Kafka topics in Spark using PySpark. Upgraded Glue jobs from 2.0 to 4.0 using cloud formation templates and migrated jobs to production. Experience integrating LLM using OpenAI API.
Data Engineer
Larsen Toubro InfotechSep, 2019 - Feb, 20222 yr 5 months
Achieved real-time data reconciliation using Kafka, Cassandra, Logstash, Elasticsearch, Kibana. Upgraded Elasticsearch, Logstash, Kibana, Filebeat to latest version. Implemented Authentication, Authorization, SSL in ELK stack. Automated deployment of config files using shell script. Designed dashboards in Kibana for monitoring logs using Filebeat. Automated committing of git artifacts, saving developers time by 20%. Built real-time data pipeline to automate data collection from Kafka. Increased system performance by 30% based on JVM heap size, worker, threads.
Achievements
- Developed ETL data processing pipeline using Python, Elasticsearch, Kibana
- Engineered and deployed an ETL pipeline in AWS
- Automated Backup reporting thus reducing manual efforts by 40%
- Implemented best practices for clear logging mechanism
- Established POC to read data from Kafka topics in Spark using Spark
- Achieved Realtime data reconciliation using Kafka, Cassandra, Logstash, Elasticsearch, Kibana
- Upgraded Elasticsearch, Logstash, Kibana, File beat to latest version
- Implemented Authentication, Authorization, SSL in ELK stack
- Automated deployment of config files using shell script
- Designed dashboards in Kibana for monitoring logs using File beats
- Automated committing of git artifacts thus saving developers time by 20%
- Built real time data pipeline to automate data collection from Kafka
- Increased performance of system by 30% based on parameters JVM heap size, worker, threads
- Appreciated by clients for exceeding the clients requirements.
- Automated Backup reporting thus reducing manual efforts by 40%.
- Optimized resource allocation for 30% efficiency boost.
- Automated committing of git artifacts thus saving developers time by 20%.
- Increased performance of system by 30% based on parameters JVM heap size, worker, threads.
- Engineered and deployed an ETL pipeline in AWS using AWS Glue, S3, Redshift, and Power BI, optimizing resource allocation for 30% efficiency boost.
- Implemented best practices for clear logging mechanism.
- Developed ETL data processing pipeline using Python, Elasticsearch, Kibana. Reading multiple CSV files, performing transformations on them using NumPy, Pandas and delivering useful business insights.
- Engineered and deployed an ETL pipeline in AWS using AWS Glue, S3, Redshift, and Power BI, extracting data from Jira API to analyze teams, boards, and sprints, optimizing resource allocation for a 30% efficiency boost.
- Integrated data from APIs to build analytical dashboards.
- Effectively communicated client needs, guiding team members towards meeting project requirements.
- Working on POC using Data Migration service in AWS to migrate data from Oracle to Redshift.
- Established POC to read data from Kafka topics in spark using Spark.
- Achieved Realtime data reconciliation using Kafka, Cassandra, Logstash, Elasticsearch, Kibana.
- Upgraded Elasticsearch, Logstash, Kibana, Filebeat to latest version.
- Implemented Authentication, Authorization, SSL in ELK stack.
- Automated deployment of config files using shell script.
- Designed dashboards in Kibana for monitoring logs using Filebeats.
- Built real-time data pipeline to automate data collection from Kafka.
Major Projects
1Projects
Education
Bachelor of Engineering
Walchand Institute of Technology (2019)
Certifications
Nptel oops
Ibm cognitive class: big data 101
Nptel cloud computing
Nptel joy of computing using python