Abdul Aziz

Concord Systems Usa

Bolingbrook

12.2021 - Current

1. Data Pipeline Development & ETL

Designed, developed, and optimized end-to-end ETL pipelines for data extraction, transformation, and loading from multiple data sources into Amazon Redshift, S3, and RDS.
Developed scalable data workflows using AWS Lambda and AWS Glue to automate data processing and ensure smooth data integration across various platforms.
Utilized Databricks for building high-performance ETL pipelines, running distributed data processing workloads, and optimizing Spark jobs for large-scale data transformation.
Integrated Amazon S3 with other AWS services for storing large datasets and leveraging AWS Glue for managing metadata and schema evolution.

2. Cloud Data Architecture & AWS Services

Architected and deployed scalable data architectures using AWS services like EC2, FSx, RDS, S3, and CloudFront to store, process, and distribute data at scale.
Utilized Amazon RDS for relational database management and implemented AWS FSx for high-performance file systems, ensuring data processing efficiency.
Optimized data access and distribution through AWS CloudFront, enabling low-latency content delivery to geographically distributed users.

3. Data Quality, Monitoring, & Optimization

Established data quality checks and implemented monitoring in AWS CloudWatch to ensure the integrity and transparency of data pipelines.
Optimized SQL queries and ETL jobs for performance and resource efficiency, reducing processing times by X% using Databricks and AWS Lambda for serverless data operations.
Utilized AWS Glue for metadata management and automated data cleaning, reducing errors in the data pipeline and ensuring high-quality data availability for analytics.

4. Automation & Scripting

Developed Python scripts to automate data extraction, transformation, and loading, integrating with AWS Lambda and S3 to ensure seamless data flow and minimize manual intervention.
Automated data validation processes and pipeline monitoring, leveraging AWS CloudWatch for proactive issue resolution and ensuring system uptime.

5. Collaboration & Cross-Functional Work

Collaborated with data scientists, analysts, and business stakeholders to understand data requirements, developing optimized data solutions using AWS and Databricks.
Worked closely with DevOps teams to implement CI/CD pipelines using Azure DevOps, enabling automated deployment and continuous integration for data engineering workflows.

6. Data Analytics & Visualization

Designed and developed interactive dashboards and reports using Tableau and Power BI, providing stakeholders with real-time insights into key business metrics and performance.
Integrated Databricks with Tableau for advanced analytics and visualizations, allowing data scientists and analysts to access processed data directly from the Databricks environment.
Leveraged Power BI to create custom business intelligence reports and data visualizations, allowing non-technical users to explore and interpret complex data without relying on technical teams.

7. Big Data Technologies

Built and maintained data pipelines for processing structured and unstructured data from various sources, using Databricks for distributed data processing.
Utilized Apache Spark on Databricks for high-performance data processing, significantly improving data processing times for large datasets.

8. Database Management & Optimization

Proficient in Amazon RDS for managing relational databases, ensuring performance, backup, and scalability based on workload demands.
Managed and optimized data stored in MongoDB, designing data models and optimizing queries for efficient data retrieval in AWS environments.

9. Data Security & Compliance

Implemented data encryption protocols for secure data storage and transfer using AWS KMS, ensuring compliance with data security and privacy standards, such as GDPR and HIPAA.
Managed IAM roles and permissions to enforce data governance and ensure secure data access across teams and systems.

10. CI/CD & Automation

Implemented CI/CD pipelines using Azure DevOps, automating testing, building, and deployment of data engineering solutions, and ensuring continuous delivery across development, staging, and production environments.
Integrated Azure DevOps with AWS infrastructure for seamless deployment of data engineering projects, ensuring rapid release cycles, and high-quality data products.

Summary