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Qualifications
- Python : Proven expertise in leveraging Python for data manipulation, scripting, and the development of robust data-driven applications.
- PySpark : In-depth experience in building and optimizing scalable data processing workflows using PySpark for distributed data transformation.
- SQL : Strong command of SQL with the ability to craft complex queries, perform performance tuning, and design efficient relational databases.
- AWS : Practical experience in architecting, deploying, and maintaining data solutions on AWS platforms such as S3 , EMR , Glue , Lambda , and more.
- Data Engineering : Comprehensive understanding of data warehousing architectures, ETL/ELT strategies, and best practices in designing resilient data pipelines.
- Professional Skills : Demonstrated strength in analytical thinking, problem-solving, and clear communication.
Capable of working independently as well as in a collaborative team setting.
Preferred skills
- Airflow : Hands-on experience in orchestrating, scheduling, and monitoring data workflows using Apache Airflow.
- Snowflake : Working knowledge of Snowflake’s cloud data warehousing features and its use in analytical processing.
- Version Control : Proficient in using Git-based version control systems such as Bitbucket for collaborative code management and CI/CD workflows.
Job details
- Seniority level : Mid-Senior level
- Employment type : Full-time
- Job function : Information Technology
- Industries : IT Services and IT Consulting
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