
Chief Data Engineering Specialist
2 weeks ago
This role involves designing, developing, and maintaining scalable data pipelines using Python and SQL. You will be responsible for implementing ETL processes to clean, enrich, and transfer data across systems and cloud environments.
A key aspect of this position is collaboration with data scientists, analysts, and stakeholders to ensure data quality and availability. You will also be required to integrate AWS services (e.g., S3, Redshift, Glue, Lambda) into data workflows and optimize database performance and query efficiency.
The ideal candidate will have hands-on experience with Python, SQL, and AWS data services. They should also possess strong problem-solving skills and effective communication and collaboration abilities.
Key Responsibilities:
- Data Pipeline Development: Design, develop, and maintain scalable data pipelines.
- ETL Processes: Implement ETL processes to clean, enrich, and transfer data.
- Data Collaboration: Collaborate with data scientists, analysts, and stakeholders.
- AWS Integration: Integrate AWS services into data workflows.
- Database Optimization: Optimize database performance and query efficiency.
Required Skills & Experience:
- Python Proficiency: Proficiency in Python for data processing and scripting.
- SQL Skills: Strong SQL skills for data extraction, transformation, and manipulation.
- ETL Experience: Experience with ETL processes and working with large datasets.
- AWS Familiarity: Familiarity with AWS services such as S3, Redshift, Glue, Lambda.
- Data Warehousing: Basic understanding of data warehousing concepts and best practices.
Preferred Qualifications:
- AWS Expertise: Hands-on experience with AWS data services.
- Workflow Orchestration: Familiarity with workflow orchestration tools like Apache Airflow.
- Data Modeling: Exposure to data modeling and database design.
- Big Data Technologies: Knowledge of big data technologies.
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