Data Engineer

Rate, USD
Not specified
Work schedule
Full Time, Contract,
Language skills
English, Hindi
Available for Hire
Yes
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About me

The team at University of Illinois Springfield embraced my contributions as we developed a sentiment analysis model, achieving over 85% accuracy and reducing training times by 30% through innovative optimization. My role in integrating sentiment analysis APIs into three applications significantly enhanced user engagement and satisfaction. With a Master’s in Data Analytics in progress, my academic insights complement my practical expertise.

At Aarmec Technologies, my tenure was marked by designing robust data pipelines and optimizing database functions within AWS Cloud environments. The competencies I honed there, coupled with my current academic pursuits, underscore my commitment to advancing cloud analytics and data engineering solutions that are critical in today’s data-driven landscape.




Education

Jan 2023 - May 2024 Masters in Data Analytics @ University of Illinois Springfield
June 2017 - July 2020 Computer Science and Engineering @ Global Academy of Technology

Experience

Aug 2020 - Dec 2022 Data Engineer @ Aarmec Technologies

As a Data Engineer with over two years of hands-on experience, I have developed and managed robust data architectures that enable seamless data integration, storage, and analysis. My expertise spans designing efficient data pipelines, optimizing databases, and ensuring high data quality and governance. I am proficient in leveraging both traditional and cutting-edge technologies to deliver scalable data solutions that meet the evolving needs of businesses.

Key Responsibilities:

1. Data Pipeline Development:
– Designed and deployed scalable data pipelines using modern ETL frameworks, ensuring reliable data integration from multiple sources.
– Automated data ingestion and transformation processes, significantly reducing manual intervention and improving efficiency.

2. Database Management:
– Developed and optimized complex SQL and NoSQL databases to ensure high performance and reliability.
– Implemented effective data partitioning, indexing, and archiving strategies to enhance data retrieval and storage efficiency.

3. ETL Processes:
– Created and maintained robust ETL processes, ensuring timely and accurate data loading and transformation.
– Troubleshot and resolved ETL job failures, minimizing downtime and maintaining data consistency.

4. Collaboration and Communication:
– Collaborated closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions that meet business objectives.
– Translated complex technical concepts into clear, actionable insights for non-technical team members.

5. Data Quality and Governance:
– Implemented data governance policies to ensure compliance with data privacy and security standards.
– Monitored data access and usage to prevent unauthorized access and ensure data integrity.As a Data Engineer with over two years of hands-on experience, I have developed and managed robust data architectures that enable seamless data integration, storage, and analysis. My expertise spans designing efficient data pipelines, optimizing databases, and ensuring high data quality and governance. I am proficient in leveraging both traditional and cutting-edge technologies to deliver scalable data solutions that meet the evolving needs of businesses. Key Responsibilities: 1. Data Pipeline Development: – Designed and deployed scalable data pipelines using modern ETL frameworks, ensuring reliable data integration from multiple sources. – Automated data ingestion and transformation processes, significantly reducing manual intervention and improving efficiency. 2. Database Management: – Developed and optimized complex SQL and NoSQL databases to ensure high performance and reliability. – Implemented effective data partitioning, indexing, and archiving strategies to enhance data retrieval and storage efficiency. 3. ETL Processes: – Created and maintained robust ETL processes, ensuring timely and accurate data loading and transformation. – Troubleshot and resolved ETL job failures, minimizing downtime and maintaining data consistency. 4. Collaboration and Communication: – Collaborated closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions that meet business objectives. – Translated complex technical concepts into clear, actionable insights for non-technical team members. 5. Data Quality and Governance: – Implemented data governance policies to ensure compliance with data privacy and security standards. – Monitored data access and usage to prevent unauthorized access and ensure data integrity.


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