Somos una empresa líder de gestión de capital humano y servicios tecnológicos con más de 15 años en el mercado nacional y Centro América, ofreciendo un valor agregado y solución a los procesos de consultoría de TI, atracción de talento, pruebas de Software y centro de desarrollo.

Teniendo siempre la satisfacción de nuestros clientes y el desempeño profesional de nuestros colaboradores. 

En Getecsa estamos comprometidos a brindar la mejor experiencia, a seguir innovando y dirigirnos con honestidad, respeto y compromiso. 

¡La oportunidad de crecer esta en tu manos.. únete a nuestro equipo de trabajo!.

DATA QA

Education: B.E. Computer Science/IT degree (or any other engineering discipline)     

Experience: 3+ years     

Position Requirements:

• Experience with data QA and ETL/ELT (Data Pipelines) QA  
• Proficient in SQL (analytical functions, trending, windowing) - Traditional (For e.g., MSSQL, Oracle, PostgreSQL) Or Columnar (Like Vertica, Amazon Redshift)  
• Experience working closely with teams outside of IT (i.e., Business Intelligence, Marketing, AdOps, Sales)  
• Strong understanding of the Web analytics, metrics, KPIs and reporting  
• Experience with automating regression tests, reporting platforms (For e.g. Tableau or Pentaho BI) and ETL tools (For eg. Pentaho or Talend) will be an added advantage  
• Understanding of Ad stack, Email data and data (Ad Servers, DSM, DMP, etc) is good to have  

Role & Responsibilities:

• Performing statistical tests on large datasets to determine data quality and integrity.  
• Evaluating system performance and design, as well as its effect on data quality.  
• Collaborating with database developers to improve data collection and storage processes.  
• Running data queries to identify coding issues and data exceptions, as well as cleaning data.  
• Gathering data from primary or secondary data sources to identify and interpret trends.  
• Reporting data analysis findings to management to inform business decisions and prioritize information system needs.  
• Documenting processes and maintaining data records.  
• Adhering to best practices in data analysis and collection.  
• Keeping abreast of developments and trends in data quality analysis.