Key Skills:AWS (S3, Redshift, Glue, Lambda), AWS QuickSight, Python, SQL, Bash Shell, ETL, Data Pipelines, Data Visualization, Data Cleansing & Transformation, Pyspark, Kafka (Optional), Data Governance
Roles & Responsibilities:
- Develop, maintain, and optimize data pipelines and ETL processes using AWS services such as S3, Redshift, Glue, and Lambda
- Design and implement data workflows for both event-driven and batch processing
- Create insightful dashboards and reports using AWS QuickSight, integrating with existing products and data sources
- Write and maintain scripts in Python and Bash for automation, data extraction, transformation, and loading
- Perform data cleansing, transformation, and validation to ensure accuracy and consistency
- Collaborate with business stakeholders to gather requirements and deliver actionable insights
- Monitor and optimize data systems for performance, scalability, and reliability
- Ensure adherence to data governance policies and maintain data quality throughout the lifecycle
- Stay updated with emerging technologies, best practices in data engineering, and BI/data visualization tools
Experience Required:
- 4 - 7 years of experience in data engineering, data analytics, or related roles
- Hands-on experience with AWS services for data storage, processing, and transformation
- Strong scripting skills in Python and Bash for automation and data manipulation
- Practical experience with SQL and data modeling
- Experience building dashboards and visualizations using AWS QuickSight or similar BI tools
- Familiarity with Pyspark, Kafka, or other big data tools is a plus
- Strong problem-solving, analytical, and communication skills
Education:B.Tech M.Tech (Dual), B.E., B.Tech, M. Tech
