Hybrid Data Engineer (19000000 COP/Mes) Data Engineer (19000000 COP/Mes)
Listopro · Argentina (Remote) · nan, · Hybrid
About the job
Responsibilities
About the position
Minimum qualifications
About the position
- Ensure data accuracy, integrity, privacy, security, and compliance through rigorous quality control procedures.
- Monitor and analyze the performance of data systems, implementing optimization strategies as needed. Develop ETL (extract, transform, load) processes to help extract and manipulate data from multiple sources.
- Acquire, clean, and prepare relevant datasets for analysis and reporting.
- Document processes, results, and methodologies to ensure clarity and facilitate maintenance.
- Collaborate with data scientists, analysts, and other stakeholders to understand data needs and provide integrated solutions.
- Work with version control systems, specifically Git, to manage codebase.
- Ensure effective data monitoring and implement improvements as necessary.
- Stay updated with the latest industry trends and technologies to ensure best practices are applied.
- Develop and maintain data pipelines using technologies such as Snowflake, Python and workflow management tools (e.g., Airflow).
- Utilize advanced SQL skills for complex queries
Minimum qualifications
- Bachelor's degree in Computer Science, Engineering, or related fields.
- 1-2 years of industry experience as a Data Engineer or in a similar role.
- Proficient in object-oriented programming languages, particularly Python, and experience with related libraries such as Pandas, Dask, NumPy, and Matplotlib.
- Advanced SQL skills and experience with relational databases and database design.
- Experience working with cloud data warehouse solutions (e.g., Snowflake, Redshift, BigQuery, Azure).
- Proficiency in using data pipelines and workflow management tools (e.g., Airflow).
- Experience working with version control systems, specifically Git.
- Proven experience in developing and maintaining ETL/ELT pipelines.
- Expertise in pre-processing and cleaning large datasets for training models
