SAS Data Analytics & Quality Analyst

Jobtailor; Madrid; Presencial; EUR 32.000 - 42.000; Jornada completa

Descripción de la vacante

Jobtailor in Madrid is seeking a data quality analyst to ensure data integrity across multiple sources and support QA for data offerings.

The role requires at least one year in a data analytics role, SAS programming skills, and proficiency with SQL, Excel, and related tools. You will collaborate with internal teams and clients to address data exceptions and drive process improvements.

A scientific degree is preferred, with strong attention to detail and communication skills essential for success.

Formación

• Bachelor’s degree in a scientific field (statistics preferred).
• At least one year of experience in data analytics or a similar role.
• SAS programming skills are required.
• Experience with SQL or R is a plus.

Responsabilidades

• Ensure data integrity and high-quality outputs across data sources.
• Support quality control and QA for data products.
• Perform standard data quality checks and identify patterns using statistics.
• Evaluate business data to ensure alignment with local market rules.
• Analyse processes and suggest opportunities for improvement.
• Document procedures to support team consistency.
• Assist in resolving data exceptions and respond to client queries.
• Maintain and track KPIs to monitor performance and accuracy.
• Provide support for clients and business initiatives when required.

Conocimientos

Data analytics

Analytical thinking

Communication

Problem solving

Detail-oriented

Educación

Bachelor’s degree in statistics or a related field

Herramientas

SAS

SQL

R

Excel

PowerPoint

Access

Descripción del empleo

Jobtailor in Madrid is seeking a data quality analyst to ensure data integrity across multiple sources and support QA for data offerings.

The role requires at least one year in a data analytics role, SAS programming skills, and proficiency with SQL, Excel, and related tools. You will collaborate with internal teams and clients to address data exceptions and drive process improvements.

A scientific degree is preferred, with strong attention to detail and communication skills essential for success.