Posted:2 months ago|
Platform:
On-site
Full Time
- Design, develop, and maintain scalable ETL pipelines for structured and unstructured data.
- Work with Python and PySpark to build efficient data processing frameworks.
- Implement data ingestion, transformation, and integration workflows across multiple systems.
- Leverage AWS Cloud services (S3, Glue, EMR, Redshift, Lambda, Athena, etc.) for data
engineering solutions.
- Optimize data pipelines for performance, scalability, and cost efficiency.
- Ensure data quality, integrity, and governance across all data processes.
- Collaborate with data scientists, analysts, and business stakeholders to provide reliable data
solutions.
- Troubleshoot, debug, and resolve performance bottlenecks in data workflows.
- Implement CI/CD practices and contribute to automation in data engineering processes.
- Stay updated on emerging cloud-native and big data technologies to recommend improvements.
- 6–8 years of experience as a Data Engineer or in a similar role.
- Strong proficiency in Python programming for data engineering.
- Hands-on experience with PySpark for big data processing.
- Solid expertise in ETL development and data pipeline automation.
- In-depth experience with AWS Cloud services (S3, Glue, EMR, Redshift, Lambda, Athena,
CloudWatch).
- Strong knowledge of SQL and relational databases.
- Good understanding of data warehousing concepts and performance optimization.
- Familiarity with CI/CD pipelines, Git, and DevOps practices for data workflows.
- Excellent problem-solving skills and ability to work independently as well as in a collaborative
environment.
- Strong communication skills to work with global stakeholders.
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