Data Qa

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 experiência, 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**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.