Head Data Engineer

• Jan 2023 - Present

1. Collaborate with stakeholders to define cloud requirements and design scalable and cost-effective GCP solutions. 2. Lead the planning and implementation of GCP infrastructure, considering best practices for security, performance, and reliability. 3. Design and implement infrastructure as code (IAC) using tools like Terraform to automate the provisioning of cloud resources. 4. Design and implement infrastructure as code (IAC) using tools like Terraform to automate the provisioning of… Show more 1. Collaborate with stakeholders to define cloud requirements and design scalable and cost-effective GCP solutions. 2. Lead the planning and implementation of GCP infrastructure, considering best practices for security, performance, and reliability. 3. Design and implement infrastructure as code (IAC) using tools like Terraform to automate the provisioning of cloud resources. 4. Design and implement infrastructure as code (IAC) using tools like Terraform to automate the provisioning of cloud resources. 5.Develop cloud architecture blueprints, ensuring alignment with business goals and technical requirements. 6. Implement security best practices and compliance requirements within the GCP environment. 7. Implement the data pipeline using different services like BigQuery, dataflow, composer, cloud functions and storage. Show less 1. Collaborate with stakeholders to define cloud requirements and design scalable and cost-effective GCP solutions. 2. Lead the planning and implementation of GCP infrastructure, considering best practices for security, performance, and reliability. 3. Design and implement infrastructure as code (IAC) using tools like Terraform to automate the provisioning of cloud resources. 4. Design and implement infrastructure as code (IAC) using tools like Terraform to automate the provisioning of… Show more 1. Collaborate with stakeholders to define cloud requirements and design scalable and cost-effective GCP solutions. 2. Lead the planning and implementation of GCP infrastructure, considering best practices for security, performance, and reliability. 3. Design and implement infrastructure as code (IAC) using tools like Terraform to automate the provisioning of cloud resources. 4. Design and implement infrastructure as code (IAC) using tools like Terraform to automate the provisioning of cloud resources. 5.Develop cloud architecture blueprints, ensuring alignment with business goals and technical requirements. 6. Implement security best practices and compliance requirements within the GCP environment. 7. Implement the data pipeline using different services like BigQuery, dataflow, composer, cloud functions and storage. Show less

Analytics Associate Manager

• Mar 2022 - Dec 2022

Role Played: Associate Manager (Data Engineering) Environment: Google Cloud Platform (App Engine, Big Query, Storage, Pub Sub, Cloud Composer, Cloud Data Flow), Python, Flask, Jira, Shell Script Project Description: The data warehouse was created by JLR to facilitate their data science team to work on multiple account management use cases. Data is being pulled from multiple diverse data sources including their own marketing entity platform data on S3 Bucket. JLR product rules… Show more Role Played: Associate Manager (Data Engineering) Environment: Google Cloud Platform (App Engine, Big Query, Storage, Pub Sub, Cloud Composer, Cloud Data Flow), Python, Flask, Jira, Shell Script Project Description: The data warehouse was created by JLR to facilitate their data science team to work on multiple account management use cases. Data is being pulled from multiple diverse data sources including their own marketing entity platform data on S3 Bucket. JLR product rules database and user activity (Contract Event Log) from SFTP server co-located in a big query. 4 Step Approach for this ETL Pipeline: 5. Pull data from RDBMS, S3 Bucket & internal SFTP and put them into a raw GCS bucket. 6. Create a Data Flow job to process the above data and place them into a curated GCS bucket. 7. Load the data into google Big Query. 8. Create a Flask Application for reporting and billing to further use that data by account managers Show less Role Played: Associate Manager (Data Engineering) Environment: Google Cloud Platform (App Engine, Big Query, Storage, Pub Sub, Cloud Composer, Cloud Data Flow), Python, Flask, Jira, Shell Script Project Description: The data warehouse was created by JLR to facilitate their data science team to work on multiple account management use cases. Data is being pulled from multiple diverse data sources including their own marketing entity platform data on S3 Bucket. JLR product rules… Show more Role Played: Associate Manager (Data Engineering) Environment: Google Cloud Platform (App Engine, Big Query, Storage, Pub Sub, Cloud Composer, Cloud Data Flow), Python, Flask, Jira, Shell Script Project Description: The data warehouse was created by JLR to facilitate their data science team to work on multiple account management use cases. Data is being pulled from multiple diverse data sources including their own marketing entity platform data on S3 Bucket. JLR product rules database and user activity (Contract Event Log) from SFTP server co-located in a big query. 4 Step Approach for this ETL Pipeline: 5. Pull data from RDBMS, S3 Bucket & internal SFTP and put them into a raw GCS bucket. 6. Create a Data Flow job to process the above data and place them into a curated GCS bucket. 7. Load the data into google Big Query. 8. Create a Flask Application for reporting and billing to further use that data by account managers Show less

Associate Manager

• Nov 2021 - Mar 2022

Role Played: Associate Manager(Azure Cloud & Python Developer) Environment: Azure DevOps, Azure Functions , Azure Blob Storage, Azure Data Bricks, Python 3 Step: 1. Check the health of code already written in python integrated with Azure DevOps. 2. Bug Fix and Integrate the logger. 3. Gather the new requirement and update the code by following standard practice in Azure DevOps. Role Played: Associate Manager(Azure Cloud & Python Developer) Environment: Azure DevOps, Azure Functions , Azure Blob Storage, Azure Data Bricks, Python 3 Step: 1. Check the health of code already written in python integrated with Azure DevOps. 2. Bug Fix and Integrate the logger. 3. Gather the new requirement and update the code by following standard practice in Azure DevOps.

Marketing Strategist and Analyst

• Jun 2019 - Nov 2021

Role Played: Data Engineer Environment: Google Cloud Platform (Big Query, Storage, Cloud Computing, Cloud Functions, Pub Sub, Scheduler, Cloud Run, Data Flow, Cloud Composer, Data Studio), Python, Shell Script Project Description: This is the project where I need to create everything from scratch.Create ETL pipeline and ingest data from multiple platforms to big Query (data warehouse). This project involves various GCP services. Getting data from multiple sources and load into… Show more Role Played: Data Engineer Environment: Google Cloud Platform (Big Query, Storage, Cloud Computing, Cloud Functions, Pub Sub, Scheduler, Cloud Run, Data Flow, Cloud Composer, Data Studio), Python, Shell Script Project Description: This is the project where I need to create everything from scratch.Create ETL pipeline and ingest data from multiple platforms to big Query (data warehouse). This project involves various GCP services. Getting data from multiple sources and load into different GCS buckets in the form of AVRO and restructure them and save into another flat csv files. Trigger the jobs written in cloud functions as soon as the data loaded into GCS and load into data ware house. 4 Step Approach for this ETL Pipeline: 1. Written different API scripts in python and scheduled the jobs using compute engine. 2. Extract data from multiple platforms and dump into google cloud storage bucket. 3. Cloud functions to trigger data flow Job to clean up data and load into Big Query. 4. Visualize the data using Data Studio, Power BI and Tableau. Show less Role Played: Data Engineer Environment: Google Cloud Platform (Big Query, Storage, Cloud Computing, Cloud Functions, Pub Sub, Scheduler, Cloud Run, Data Flow, Cloud Composer, Data Studio), Python, Shell Script Project Description: This is the project where I need to create everything from scratch.Create ETL pipeline and ingest data from multiple platforms to big Query (data warehouse). This project involves various GCP services. Getting data from multiple sources and load into… Show more Role Played: Data Engineer Environment: Google Cloud Platform (Big Query, Storage, Cloud Computing, Cloud Functions, Pub Sub, Scheduler, Cloud Run, Data Flow, Cloud Composer, Data Studio), Python, Shell Script Project Description: This is the project where I need to create everything from scratch.Create ETL pipeline and ingest data from multiple platforms to big Query (data warehouse). This project involves various GCP services. Getting data from multiple sources and load into different GCS buckets in the form of AVRO and restructure them and save into another flat csv files. Trigger the jobs written in cloud functions as soon as the data loaded into GCS and load into data ware house. 4 Step Approach for this ETL Pipeline: 1. Written different API scripts in python and scheduled the jobs using compute engine. 2. Extract data from multiple platforms and dump into google cloud storage bucket. 3. Cloud functions to trigger data flow Job to clean up data and load into Big Query. 4. Visualize the data using Data Studio, Power BI and Tableau. Show less

Analytics Engineer

• Sep 2018 - Jun 2019

Role Played: Developer Environment: Google Cloud Platform (App Engine, Big Query, Storage), Python, Flask, Jira, Shell Script Project Description: The data warehouse was created by SAGE to facilitate their data science team to work on multiple account management use cases. Data is being pulled from multiple diverse data source including their own marketing entity platform data on S3 Bucket. SAGE product rules database and user activity (Contract Event Log) from SFTP server… Show more Role Played: Developer Environment: Google Cloud Platform (App Engine, Big Query, Storage), Python, Flask, Jira, Shell Script Project Description: The data warehouse was created by SAGE to facilitate their data science team to work on multiple account management use cases. Data is being pulled from multiple diverse data source including their own marketing entity platform data on S3 Bucket. SAGE product rules database and user activity (Contract Event Log) from SFTP server co-located in big query. 4 Step Approach for this ETL Pipeline: 1. Pull data from RDBMS, S3 Bucket & internal SFTP and put them into raw GCS bucket. 2. Create Data Flow job to process above data and place them into curate GCS bucket. 3. Load the data into google Big Query. 4. Create a Flask Application for reporting and billing to further use that data by account managers. Show less Role Played: Developer Environment: Google Cloud Platform (App Engine, Big Query, Storage), Python, Flask, Jira, Shell Script Project Description: The data warehouse was created by SAGE to facilitate their data science team to work on multiple account management use cases. Data is being pulled from multiple diverse data source including their own marketing entity platform data on S3 Bucket. SAGE product rules database and user activity (Contract Event Log) from SFTP server… Show more Role Played: Developer Environment: Google Cloud Platform (App Engine, Big Query, Storage), Python, Flask, Jira, Shell Script Project Description: The data warehouse was created by SAGE to facilitate their data science team to work on multiple account management use cases. Data is being pulled from multiple diverse data source including their own marketing entity platform data on S3 Bucket. SAGE product rules database and user activity (Contract Event Log) from SFTP server co-located in big query. 4 Step Approach for this ETL Pipeline: 1. Pull data from RDBMS, S3 Bucket & internal SFTP and put them into raw GCS bucket. 2. Create Data Flow job to process above data and place them into curate GCS bucket. 3. Load the data into google Big Query. 4. Create a Flask Application for reporting and billing to further use that data by account managers. Show less

Business Intelligence Team Lead

• Aug 2015 - Sep 2018

Automate manual process using python, and design tools which generates Reports/dashboards including charts/tables. Automate task in excel using excel advance functions. Lead & mentoring the team of analysts and provide global support in different time zone. Mentor team members and manage work allocation as well as track report delivery globally. Understand digital media industry trends, and develop in-depth analysis and vertical-based. Reporting solutions at… Show more Automate manual process using python, and design tools which generates Reports/dashboards including charts/tables. Automate task in excel using excel advance functions. Lead & mentoring the team of analysts and provide global support in different time zone. Mentor team members and manage work allocation as well as track report delivery globally. Understand digital media industry trends, and develop in-depth analysis and vertical-based. Reporting solutions at advertiser & publisher level. Writing complex SQL queries. Working with different team to get their requirements and develop reports using excel. Provide direction to development team, and enforce best standard and practice.

Sr Business Analyst

• May 2014 - Oct 2015

Business Analyst

• May 2013 - Apr 2014

Data Analyst

• Feb 2011 - Apr 2013