Pervez Rasool Sayyed

AI Researcher

Mumbai, Maharashtra, India5 yrs 8 mos experience

Highly Stable

Key Highlights

Expert in cloud migration and data modernization.
Proven track record in building scalable data pipelines.
Strong background in AWS and data engineering.

Stackforce AI infers this person is a Cloud Data Engineer with expertise in AWS and data transformation.

Contact

Skills

Core Skills

Cloud MigrationData ModernizationData IngestionData TransformationCloud Database ManagementDatabase DesignAws ManagementEtl DevelopmentDatabase ManagementData Migration

Other Skills

AWSAWS CLIAWS CloudAWS Command Line Interface (CLI)AWS GlueAWS LambdaAWS Step FunctionsAirflowAmazon AthenaAmazon Elastic MapReduce (EMR)Amazon RedshiftAmazon Relational Database Service (RDS)Amazon S3Amazon Web Services (AWS)Apache Airflow

About

Versatile and value-driven professional with all rounder skillset and accomplishments developing technology strategy, conducting requirement analysis, aligning cloud services with business needs and architecting solutions for everyday business problems. Organized, creative problem solver while leading cross functional teams for challenging Agile projects and maintaining multi-agency stakeholders relationships.

Experience

5 yrs 8 mos

Total Experience

2 yrs 2 mos

Average Tenure

1 yr 4 mos

Current Experience

Capgemini

3 roles

Spectrum Mobile Data Hub

Oct 2025 – Present · 8 mos · United States · Remote

Data Modernization & Cloud Migration Project

Promoted

Jan 2025 – Sep 2025 · 8 mos · United States · Remote

Technologies: AWS Glue, PySpark, AWS Lambda, Step Functions, Amazon S3, Amazon Redshift, IBM DataStage
Overview:
Led the end-to-end modernization and migration of legacy data processing workflows from IBM DataStage to a cloud-native architecture on AWS, significantly enhancing scalability, performance, and maintainability.
Key Contributions:
Migrated over 365 IBM DataStage jobs to AWS Glue, modernizing the ETL infrastructure using serverless and distributed processing.
Designed and implemented a modular, event-driven architecture using AWS Step Functions and Lambda to orchestrate and monitor Glue job execution.
Utilized PySpark within AWS Glue for efficient, large-scale data transformation and processing.
Integrated Amazon S3 for cost-effective, scalable data storage and Amazon Redshift for high-performance data warehousing and analytics.
Automated job triggering, error handling, and monitoring using Lambda and Step Functions, ensuring seamless and reliable data workflows.
Impact:
Achieved significant improvements in processing speed, system reliability, and operational efficiency.
Reduced infrastructure maintenance overhead by adopting a fully managed, serverless architecture.
Enabled faster time-to-insight for analytics teams through improved data availability and performance.

AWS GluePySparkAWS LambdaStep FunctionsAmazon S3Amazon Redshift+3

A360 CDA Data Integration Project

Oct 2024 – Dec 2024 · 2 mos · United States · Remote

Overview:
Developed a scalable and automated data ingestion pipeline to process semi-structured data from the brown zone and transform it into structured formats for the gold zone, enabling downstream analytics and reporting.
Key Contributions:
Created Python-based ingestion scripts to parse and transform semi-structured data (e.g., JSON, XML) into structured formats.
Leveraged AWS Lambda for serverless data processing and S3 Batch Operations to efficiently handle large volumes of incoming data.
Orchestrated the workflow using AWS Step Functions, enabling conditional execution of Lambda functions based on business logic.
Implemented DynamoDB to track data ingestion status, providing a robust audit trail and real-time monitoring capabilities.
Ensured data quality, reliability, and traceability throughout the pipeline, supporting business-critical analytics use cases.
Impact:
Automated the end-to-end data flow from raw ingestion to gold zone delivery.
Improved operational visibility and auditability through integrated monitoring.
Enabled scalable and cost-effective processing of semi-structured data in a cloud-native environment.

PythonAWS LambdaS3 Batch OperationsAWS Step FunctionsDynamoDBData Ingestion+1

Nityo infotech

Cloud Database Engineer

Sep 2023 – Oct 2024 · 1 yr 1 mo · Mumbai, Maharashtra, India · On-site

Roles and Responsibilities
Cloud Database Management: Deployed and managed scalable, secure databases across AWS, Azure, and GCP, ensuring high availability and performance in cloud environments.
Database Design & Modeling: Designed robust, future-ready database architectures and data models aligned with business requirements and analytics goals.
Performance Optimization: Improved database efficiency through query tuning, indexing strategies, and proactive health monitoring to ensure optimal performance.
Data Pipeline Development: Built and maintained automated, end-to-end data pipelines using Apache Airflow, DBT, and AWS Glue for seamless data ingestion, transformation, and delivery.
CI/CD Implementation: Applied CI/CD practices to database development workflows, integrating version control, automated testing, and streamlined deployment processes.

AWSAzureGCPApache AirflowDBTCloud Database Management+1

Simple logic it private limited

3 roles

Associate Technical Consultant

Promoted

Apr 2022 – Sep 2023 · 1 yr 5 mos

Job role & Responsibilities(AWS)
1. ip whitelisting.
2. configuring lb as per requirement.
3. deploying ec2 instances as per requirement on private and public subnet.
4. Creating Security Groups
5. Creating Route table, nat gateway, internet gateway.
6. Automating uploading/download files using aws cli.
7. Automating instance using lamda function and triggering using cloudwatch.
8 Creating users on linux and windows.
9. Deploying ETL Packages in Prod & Uat Environment.
10. Creating ETL Packages to load, data and refine data as per requirements.
11. Tagging Aws Services for internal use.
12. Designing Aws Glues Pipeline to pull data into datalake from mutiple Rdbms.
13. Configuring HA-DR , VPC Peering.
14. create glue Job to pull and puch data from verious source to datalake

AWS CLIEC2Security GroupsETL PackagesAWS GlueAWS Management+1

Junior Technical Consultant

May 2020 – Mar 2022 · 1 yr 10 mos

Job role & Responsibilities
1.Manage SQL Server databases.
2.Configure and maintain database servers and processes.
3.Monitor system's health and performance.
4.Ensure high levels of performance, availability, sustainability and security.
5.Analyze, solve, and correct issues in real-time.
6.Provide suggestions for solutions.
7.Refine and automate regular processes, track issues, and document changes.
8.Assist developers with query tuning and schema refinement.
9.backup & restore activity.
10.Configuring Log Shipping and Mirroring on Prod Servers.
11. Database Migration from onprem to cloud platform like Aws, Azure..
12. Automating database backup using PowerShell.
13.Creating Logins with required access as per client demand.
14. Creating Packages to Extract, load and Tranform Data using SSIS services.
15. Configuring Mirroring HA-DR
16. Managing Azure Sql Database Server
17. Creating ETL Packages to Extract, Tranform and Load data for data warehouse
18. Migration from Oracle to Ms sql Servers

SQL ServerPowerShellSSISDatabase MigrationDatabase ManagementData Migration

Trainee Technical Consultant

Feb 2020 – Apr 2020 · 2 mos

During three months of training, I have undergone a highly professional training path wherein I have learned the following concept and hands-on.
1. MS SQL Client-Server Architecture.
2. SQL Language.
3. DR [disaster recovery model like Mirroring, Log-shipping, etc.]
4. Troubleshooting skills.
5. Windows Commandline.
6. Automating using PowerShell.

SQLWindows CommandlinePowerShell