Python Data Engineer
7 days ago
A Python Data Engineer job description typically includes the following responsibilities, qualifications, and skills:
Job Description :
Responsibilities :
Develop and maintain robust data pipelines to extract, transform, and load (ETL) data from various sources into data lakes, warehouses, or databases.
Design, build, and optimize data models and schemas to support analytics and reporting requirements.
Collaborate with data scientists and analysts to understand data needs and implement solutions that enable advanced analytics and machine learning.
Ensure data quality and integrity throughout the data lifecycle by implementing data validation, monitoring, and governance processes.
Work closely with cross-functional teams to understand business requirements and translate them into technical solutions.
Continuously evaluate and adopt new technologies and tools to improve data engineering processes and efficiency.
Troubleshoot data-related issues and performance bottlenecks in production systems.
Document data pipelines, workflows, and technical specifications for reference and knowledge :
Bachelors or Masters degree in Computer Science, Engineering, or related field.
Proven experience as a data engineer or similar role, with expertise in Python programming and data manipulation libraries (e.g., pandas, NumPy).
Strong knowledge of database systems (SQL and NoSQL) and experience with data modeling and schema design.
Proficiency in building and optimizing ETL processes using tools like Apache Spark, Apache Airflow, or similar technologies.
Experience working with cloud platforms such as AWS, Azure, or Google Cloud for data storage, processing, and analytics.
Familiarity with big data technologies like Hadoop, Hive, or HBase is a plus.
Excellent problem-solving skills and the ability to work independently or as part of a team in a fast-paced environment.
Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
Skills :
Python programming
Data manipulation and analysis
Database systems (SQL and NoSQL)
ETL processes and tools
Data modeling and schema design
Cloud platforms (AWS, Azure, Google Cloud)
Big data technologies
Problem-solving
Communication
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