Remote Data QA Analyst (Azure & Databricks)

Y-Axis; Santiago; Híbrido; CLP 18.000.000 - 32.000.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Learning opportunities

Travel opportunities

Mentoring and development

Celebrations & support

Company-provided equipment

Flexible working options

Descripción de la vacante

Blend is seeking a Data QA Analyst to join our data platform team. The role focuses on ensuring data quality, reliability, and consistency across Azure and Databricks-based pipelines.

You will validate pipelines, implement automated checks, and collaborate with Data Engineering and business teams to produce accurate, production-ready data assets. Responsibilities include building a data quality framework, validating end-to-end data flows, and supporting client UAT.

Formación

• Experience with Azure-based data platforms and Databricks.
• Strong SQL skills and experience analyzing large datasets.
• Experience validating ETL/ELT processes and working with layered architectures (Bronze, Silver, Gold).
• Knowledge of data quality frameworks and testing methodologies.

Responsabilidades

• Design and implement a data quality framework across Bronze, Silver, and Gold layers.
• Validate data pipelines and perform end-to-end testing.
• Collaborate with Data Engineers and stakeholders to ensure production-ready data assets.
• Support UAT with client business stakeholders and document QA processes.

Conocimientos

Azure

Databricks

SQL

Data Quality Frameworks

ETL/ELT Validation

Data Pipeline Testing

Automated Data Validation

Reconciliation

Identity Resolution

UAT Support

Analytical Thinking

Technical Documentation

Herramientas

Databricks

Azure

Descripción del empleo

Blend is seeking a Data QA Analyst to join our data platform team. The role focuses on ensuring data quality, reliability, and consistency across Azure and Databricks-based pipelines.

You will validate pipelines, implement automated checks, and collaborate with Data Engineering and business teams to produce accurate, production-ready data assets. Responsibilities include building a data quality framework, validating end-to-end data flows, and supporting client UAT.